From f0c7a026305ace410610c9dba771699e13dde8ea Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Mon, 7 Oct 2024 00:41:28 +0000 Subject: [PATCH 01/49] updates needed for demo Signed-off-by: Chun Tao --- comps/cores/mega/gateway.py | 6 ++++-- comps/tts/speecht5/dependency/speecht5_model.py | 3 +++ 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/comps/cores/mega/gateway.py b/comps/cores/mega/gateway.py index 115658e318..54bc6ab6dd 100644 --- a/comps/cores/mega/gateway.py +++ b/comps/cores/mega/gateway.py @@ -419,8 +419,10 @@ async def handle_request(self, request: Request): ) last_node = runtime_graph.all_leaves()[-1] - response = result_dict[last_node]["byte_str"] - + #response = result_dict[last_node]["byte_str"] + response = {"byte_str": result_dict[last_node]["byte_str"], + "text": result_dict["llm/MicroService"]["text"], + "query": result_dict["asr/MicroService"]["query"]} return response diff --git a/comps/tts/speecht5/dependency/speecht5_model.py b/comps/tts/speecht5/dependency/speecht5_model.py index 8a800d68d8..20ae8f0d40 100644 --- a/comps/tts/speecht5/dependency/speecht5_model.py +++ b/comps/tts/speecht5/dependency/speecht5_model.py @@ -51,6 +51,9 @@ def __init__(self, device="cpu"): def split_long_text_into_batch(self, text, batch_length=128): """Batch the long text into sequences of shorter sentences.""" + # ctao - Replace AI with "A, I" + text = text.replace("AI", "A, I") + text = text.replace("OPEX", "OPEA") res = [] hitted_ends = [",", ".", "?", "!", "。", ";", " "] idx = 0 From dbf8ad3bd7dbd55b024f45305fe1f05094672b74 Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Tue, 8 Oct 2024 02:53:20 +0000 Subject: [PATCH 02/49] original pr content Signed-off-by: Chun Tao --- comps/animation/Dockerfile_hpu | 84 ++++ comps/animation/README.md | 81 ++++ comps/animation/animation.py | 223 +++++++++++ .../assets/audio/sample_question.json | 3 + comps/animation/assets/avatar1.jpg | Bin 0 -> 151228 bytes comps/animation/assets/avatar2.jpg | Bin 0 -> 161903 bytes comps/animation/assets/avatar3.png | Bin 0 -> 2643247 bytes comps/animation/assets/avatar4.png | Bin 0 -> 1016282 bytes comps/animation/assets/avatar5.png | Bin 0 -> 1752197 bytes comps/animation/assets/avatar6.png | Bin 0 -> 1631099 bytes comps/animation/assets/design.png | Bin 0 -> 173014 bytes comps/animation/assets/eg3_ref.wav | Bin 0 -> 1269838 bytes comps/animation/assets/flowchart.png | Bin 0 -> 28483 bytes comps/animation/assets/gaudi.png | Bin 0 -> 48394 bytes comps/animation/assets/opea_gh_qr.png | Bin 0 -> 20344 bytes comps/animation/assets/opea_qr.png | Bin 0 -> 25385 bytes comps/animation/assets/pallavi.png | Bin 0 -> 116360 bytes comps/animation/assets/xeon.jpg | Bin 0 -> 22686 bytes comps/animation/docker_run.sh | 4 + comps/animation/entrypoint.sh | 22 ++ comps/animation/outputs/result.mp4 | Bin 0 -> 227843 bytes comps/animation/requirements.txt | 30 ++ comps/animation/test_animation_server.py | 21 + comps/animation/utils.py | 367 ++++++++++++++++++ 24 files changed, 835 insertions(+) create mode 100644 comps/animation/Dockerfile_hpu create mode 100644 comps/animation/README.md create mode 100644 comps/animation/animation.py create mode 100644 comps/animation/assets/audio/sample_question.json create mode 100644 comps/animation/assets/avatar1.jpg create mode 100644 comps/animation/assets/avatar2.jpg create mode 100644 comps/animation/assets/avatar3.png create mode 100644 comps/animation/assets/avatar4.png create mode 100644 comps/animation/assets/avatar5.png create mode 100644 comps/animation/assets/avatar6.png create mode 100644 comps/animation/assets/design.png create mode 100644 comps/animation/assets/eg3_ref.wav create mode 100644 comps/animation/assets/flowchart.png create mode 100644 comps/animation/assets/gaudi.png create mode 100644 comps/animation/assets/opea_gh_qr.png create mode 100644 comps/animation/assets/opea_qr.png create mode 100644 comps/animation/assets/pallavi.png create mode 100644 comps/animation/assets/xeon.jpg create mode 100644 comps/animation/docker_run.sh create mode 100644 comps/animation/entrypoint.sh create mode 100644 comps/animation/outputs/result.mp4 create mode 100644 comps/animation/requirements.txt create mode 100644 comps/animation/test_animation_server.py create mode 100644 comps/animation/utils.py diff --git a/comps/animation/Dockerfile_hpu b/comps/animation/Dockerfile_hpu new file mode 100644 index 0000000000..3ed0447851 --- /dev/null +++ b/comps/animation/Dockerfile_hpu @@ -0,0 +1,84 @@ +# Use a base image +# FROM python:3.11-slim +FROM vault.habana.ai/gaudi-docker/1.16.2/ubuntu22.04/habanalabs/pytorch-installer-2.2.2:latest AS hpu + +# Set environment variables +ENV LANG=en_US.UTF-8 +ENV PYTHONPATH=/home:/home/user:/usr/lib/habanalabs/:/optimum-habana +ENV PYTHON=/usr/bin/python3.10 + +# Install dependencies +RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \ + yasm \ + libx264-dev + +# TO-DO: Add a user for security + +# Install GenAIComps +RUN mkdir -p /home/user/comps +COPY comps /home/user/comps +COPY comps/animation/entrypoint.sh /usr/local/bin/entrypoint.sh + +# Install ppmpeg with x264 software codec +RUN git clone https://github.com/FFmpeg/FFmpeg.git /home/user/comps/animation/FFmpeg +WORKDIR /home/user/comps/animation/FFmpeg +RUN ./configure --enable-gpl --enable-libx264 && \ + make -j$(nproc-1) && \ + make install && \ + hash -r +RUN chmod +x $(which ffmpeg) + +# # Install Wav2Lip-GFPGAN from github +# RUN git clone https://github.com/ajay-sainy/Wav2Lip-GFPGAN.git /home/user/comps/animation/src +# # Rename subfolders "Wav2Lip" and "GFPGAN" +# RUN mv /home/user/comps/animation/src/Wav2Lip-master /home/user/comps/animation/src/Wav2Lip && \ +# mv /home/user/comps/animation/src/GFPGAN-master /home/user/comps/animation/src/GFPGAN + +# # Set the PYTHONPATH environment variable +# RUN touch /home/user/comps/animation/src/Wav2Lip/__init__.py +# RUN touch /home/user/comps/animation/src/GFPGAN/__init__.py +# ENV PYTHONPATH="/home/user/comps/animation/src:/home/user/comps/animation/src/Wav2Lip:/home/user/comps/animation/src/GFPGAN:$PYTHONPATH" + +# Upgrade pip +RUN python3 -m pip install --upgrade pip + +# Install Wav2Lip from pip +RUN pip install --no-deps Wav2Lipy +RUN rm /usr/local/lib/python3.10/dist-packages/Wav2Lip/__init__.py && touch /usr/local/lib/python3.10/dist-packages/Wav2Lip/__init__.py +ENV PYTHONPATH="/usr/local/lib/python3.10/dist-packages/Wav2Lip:$PYTHONPATH" + +# Install GFPGAN from pip +RUN pip install --no-deps gfpgan +RUN touch /usr/local/lib/python3.10/dist-packages/gfpgan/__init__.py +ENV PYTHONPATH="/usr/local/lib/python3.10/dist-packages/gfpgan:$PYTHONPATH" + +# Download pre-trained models +# Install gdown +RUN pip install gdown +WORKDIR /usr/local/lib/python3.10/dist-packages +# RUN wget https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth -O Wav2Lip/face_detection/detection/sfd/s3fd.pth +RUN gdown https://drive.google.com/uc?id=1wwts8U4Wx2lpLOI9uGcUB-TLmKRIFnP8 -O Wav2Lip/face_detection/detection/sfd/s3fd.pth +RUN mkdir -p Wav2Lip/checkpoints +RUN gdown https://drive.google.com/uc?id=1mIKfu_onFKbkbrq6cKVRBblXe5bNMhd9 -O Wav2Lip/checkpoints/wav2lip.pth +RUN gdown https://drive.google.com/uc?id=1DD7dtUfNWqNoW-2Gnp78B4PyaiJ5ybhP -O Wav2Lip/checkpoints/wav2lip_gan.pth +RUN wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P gfpgan/experiments/pretrained_models + +# Install pip dependencies +RUN pip install -r /home/user/comps/animation/requirements.txt + +# Custom patches +# Modify the degradations.py file to import rgb_to_grayscale from torchvision.transforms.functional +RUN sed -i 's/from torchvision.transforms.functional_tensor import rgb_to_grayscale/from torchvision.transforms.functional import rgb_to_grayscale/' /usr/local/lib/python3.10/dist-packages/basicsr/data/degradations.py + +# Modify the core.py file to include 'hpu' in the device check +RUN sed -i "s/if 'cpu' not in device and 'cuda' not in device:/if 'cpu' not in device and 'cuda' not in device and 'hpu' not in device:/" /usr/local/lib/python3.10/dist-packages/Wav2Lip/face_detection/detection/core.py + +# To be compatible with librosa==0.10.2, instead of librosa==0.7.0 because the co-dependency numba==0.48 cannot be pip installed +RUN sed -i 's/hp.sample_rate, hp.n_fft/sr=hp.sample_rate, n_fft=hp.n_fft/' /usr/local/lib/python3.10/dist-packages/Wav2Lip/audio.py + +# Set the working directory +WORKDIR /home/user/comps/animation + +# Define the command to run when the container starts +RUN chmod +x /usr/local/bin/entrypoint.sh +ENTRYPOINT ["/usr/local/bin/entrypoint.sh"] \ No newline at end of file diff --git a/comps/animation/README.md b/comps/animation/README.md new file mode 100644 index 0000000000..e900fb0ac3 --- /dev/null +++ b/comps/animation/README.md @@ -0,0 +1,81 @@ +# Avatar Animation Microservice + +The avatar animation model is a combination of two models: Wav2Lip and GAN-based face generator (GFPGAN). The Wav2Lip model is used to generate lip movements from an audio file, and the GFPGAN model is used to generate a high-quality face image from a low-quality face image. The avatar animation microservices takes an audio piece and a low-quality face image/video as input, fuses mel-spectrogram from the audio with frame(s) from the image/video, and generates a high-quality video of the face image with lip movements synchronized with the audio. + +# 🚀1. Start Microservice with Docker (option 1) + +## 1.1 Build the Docker image + +```bash +git clone https://github.com/opea-project/GenAIComps.git +cd GenAIComps +docker build -t opea/animation:latest -f comps/animation/Dockerfile_hpu . +``` + +## 1.2. Set environment variables + +```bash +export ip_address=$(hostname -I | awk '{print $1}') +export ANIMATION_PORT=7860 +export INFERENCE_MODE='wav2clip+gfpgan' +export CHECKPOINT_PATH='/usr/local/lib/python3.10/dist-packages/Wav2Lip/checkpoints/wav2lip_gan.pth' +export FACE='assets/avatar1.jpg' +# export AUDIO='assets/eg3_ref.wav' # audio file path is optional, will use base64str as input if is 'None' +export AUDIO='None' +export FACESIZE=96 +export OUTFILE='/home/demo/ctao/forks/GenAIComps/comps/animation/outputs/result.mp4' +export GFPGAN_MODEL_VERSION=1.3 +export UPSCALE_FACTOR=1 +export FPS=10 +``` + +## 1.3. Run the Docker container + + + +```bash +docker run --privileged -d --runtime=habana --cap-add=sys_nice --net=host --ipc=host --name "animation-service" -v /var/run/docker.sock:/var/run/docker.sock -v /usr/bin/docker:/usr/bin/docker -v $(pwd):$(pwd) -w /home/user/comps/animation -e HABANA_VISIBLE_DEVICES="3" -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e PYTHON=/usr/bin/python3.10 -e INFERENCE_MODE=$INFERENCE_MODE -e CHECKPOINT_PATH=$CHECKPOINT_PATH -e FACE=$FACE -e AUDIO=$AUDIO -e FACESIZE=$FACESIZE -e OUTFILE=$OUTFILE -e GFPGAN_MODEL_VERSION=$GFPGAN_MODEL_VERSION -e UPSCALE_FACTOR=$UPSCALE_FACTOR -e FPS=$FPS -e ANIMATION_PORT=$ANIMATION_PORT opea/animation:latest +``` + +# 🚀2. Start Microservice with Python (option 2) + +Follow 1.1 and 1.2 steps from the above section to build the Docker image and set the environment variables. + +## 2.1. Run the Docker container by overriding the entrypoint + +```bash +docker run --privileged --rm -it --runtime=habana --cap-add=sys_nice --net=host --ipc=host --name "animation-service" -v /var/run/docker.sock:/var/run/docker.sock -v /usr/bin/docker:/usr/bin/docker -v $(pwd):$(pwd) -w /home/user/comps/animation -e HABANA_VISIBLE_DEVICES="3" -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e PYTHON=/usr/bin/python3.10 -e INFERENCE_MODE=$INFERENCE_MODE -e CHECKPOINT_PATH=$CHECKPOINT_PATH -e FACE=$FACE -e AUDIO=$AUDIO -e FACESIZE=$FACESIZE -e OUTFILE=$OUTFILE -e GFPGAN_MODEL_VERSION=$GFPGAN_MODEL_VERSION -e UPSCALE_FACTOR=$UPSCALE_FACTOR -e FPS=$FPS -e ANIMATION_PORT=$ANIMATION_PORT --entrypoint /bin/bash opea/animation:latest +``` + +## 2.2 Inside the container, run the following command to start the microservice + +```bash +python3 animation.py --inference_mode $INFERENCE_MODE --checkpoint_path $CHECKPOINT_PATH --face $FACE --audio $AUDIO --outfile $OUTFILE --img_size $FACESIZE -v $GFPGAN_MODEL_VERSION -s $UPSCALE_FACTOR --fps $FPS --only_center_face --bg_upsampler None +``` + +# 🚀3. Validate Microservice + +Once microservice starts, user can use below script to validate the running microservice. + +```bash +cd GenAIComps +export ip_address=$(hostname -I | awk '{print $1}') +curl http://${ip_address}:7860/v1/animation -X POST -H "Content-Type: application/json" -d @comps/animation/assets/audio/sample_question.json +``` + +or + +```bash +cd GenAIComps/comps/animation +python3 test_animation_server.py +``` + +The expected output is a message similar to the following: + +```bash +"Status code: 200" +"Check $OUTFILE for the result." +"{'id': '33dd8249228b0e011a33b449af9aa776', 'video_save_path': '/home/demo/ctao/forks/GenAIComps/comps/animation/outputs/result.mp4'}" +``` + +Please find "./outputs/result.mp4" as a reference generated video. diff --git a/comps/animation/animation.py b/comps/animation/animation.py new file mode 100644 index 0000000000..b6923a4e1a --- /dev/null +++ b/comps/animation/animation.py @@ -0,0 +1,223 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 +# Copyright (c) 2013--2023, librosa development team. +# Copyright 1999-2003 The OpenLDAP Foundation, Redwood City, California, USA. All Rights Reserved. +# Copyright (c) 2012, Anaconda, Inc. All rights reserved. + +import os +import pathlib +import platform +import subprocess +import time + +import cv2 +import ffmpeg +import numpy as np + +# Wav2Lip-GFPGAN +import requests +import Wav2Lip.audio as audio +import Wav2Lip.face_detection as face_detection +from basicsr.utils import imwrite +from gfpgan import GFPGANer +from Wav2Lip.models import Wav2Lip + +cur_path = pathlib.Path(__file__).parent.resolve() +comps_path = os.path.join(cur_path, "../") + +# Habana +import habana_frameworks.torch.core as htcore +import habana_frameworks.torch.hpu as hthpu +from utils import * + +# GenAIComps +from comps import ( + AnimationDoc, + Base64ByteStrDoc, + ServiceType, + opea_microservices, + register_microservice, + register_statistics, + statistics_dict, +) + +device = "hpu" if hthpu.is_available() else "cpu" +print("Using {} for inference.".format(device)) + +args = get_args() +print("args: ", args) + + +# Register the microservice +@register_microservice( + name="opea_service@animation", + service_type=ServiceType.ANIMATION, + endpoint="/v1/animation", + host="0.0.0.0", + port=args.port, + input_datatype=Base64ByteStrDoc, +) +@register_statistics(names=["opea_service@animation"]) +def animate(input: Base64ByteStrDoc): + start = time.time() + + if not os.path.exists("inputs"): + os.makedirs("inputs") + if not os.path.exists("temp"): + os.makedirs("temp") + if not os.path.exists("outputs"): + os.makedirs("outputs") + + print(args.face, args.audio) + if not os.path.isfile(args.face): + raise ValueError("--face argument must be a valid path to video/image file") + elif args.face.split(".")[-1] in ["jpg", "jpeg", "png"]: + full_frames = [cv2.imread(args.face)] + fps = args.fps + else: + video_stream = cv2.VideoCapture(args.face) + fps = video_stream.get(cv2.CAP_PROP_FPS) + print("Reading video frames...") + full_frames = [] + while True: + still_reading, frame = video_stream.read() + if not still_reading: + video_stream.release() + break + if args.resize_factor > 1: + frame = cv2.resize(frame, (frame.shape[1] // args.resize_factor, frame.shape[0] // args.resize_factor)) + if args.rotate: + frame = cv2.rotate(frame, cv2.ROTATE_90_CLOCKWISE()) + + y1, y2, x1, x2 = args.crop + if x2 == -1: + x2 = frame.shape[1] + if y2 == -1: + y2 = frame.shape[0] + frame = frame[y1:y2, x1:x2] + full_frames.append(frame) + + print("Number of frames available for inference: " + str(len(full_frames))) + + if args.audio != "None": + if not args.audio.endswith(".wav"): + os.makedirs("temp", exist_ok=True) + print("Extracting raw audio...") + # command = f"ffmpeg -y -i {args.audio} -strict -2 temp/temp.wav" + # subprocess.call(command, shell=True) + + ffmpeg.input(args.audio).output("temp/temp.wav", strict="-2").run(overwrite_output=True) + args.audio = "temp/temp.wav" + else: + sr, y = base64_to_int16_to_wav(input.byte_str, "temp/temp.wav") + args.audio = "temp/temp.wav" + + wav = audio.load_wav(args.audio, 16000) + mel = audio.melspectrogram(wav) + print("Mel spectrogram shape: " + str(mel.shape)) + if np.isnan(mel.reshape(-1)).sum() > 0: + raise ValueError("Mel contains nan! Using a TTS voice? Add a small epsilon noise to the wav file and try again") + + # one single video frame corresponds to 80/25*0.01 = 0.032 seconds (or 32 milliseconds) of audio + # 30 fps video will match closer to audio, than 25 fps + mel_chunks = [] + mel_idx_multiplier = 80.0 / fps + i = 0 + mel_step_size = 16 + while True: + start_idx = int(i * mel_idx_multiplier) + if start_idx + mel_step_size > len(mel[0]): + mel_chunks.append(mel[:, len(mel[0]) - mel_step_size :]) + break + mel_chunks.append(mel[:, start_idx : start_idx + mel_step_size]) + i += 1 + print(f"Length of mel chunks: {len(mel_chunks)}") + + full_frames = full_frames[: len(mel_chunks)] + + batch_size = args.wav2lip_batch_size + gen = datagen(args, full_frames.copy(), mel_chunks) + + # iterate over the generator + for i, (img_batch, mel_batch, frames, coords) in enumerate( + tqdm(gen, total=int(np.ceil(float(len(mel_chunks)) / batch_size))) + ): + if i == 0: + # load Wav2Lip model + model = load_model(args.checkpoint_path) + print("Wav2Lip Model loaded") + + # load BG sampler if needed + if args.inference_mode == "wav2clip+gfpgan" and args.bg_upsampler == "realesrgan": + model_bg_upsampler = load_bg_upsampler(args) + print("Model BG Sampler loaded") + # model_bg_upsampler = torch.compile(model_bg_upsampler, backend="hpu_backend") + # print("Model BG Sampler compiled") + else: + model_bg_upsampler = None + print("Model BG Sampler not loaded") + + # load GFPGAN model if needed + if args.inference_mode == "wav2clip+gfpgan": + model_restorer = load_gfpgan(args, model_bg_upsampler) + print("Model GFPGAN and face helper loaded") + else: + model_restorer = None + print("Model GFPGAN not loaded") + + frame_h, frame_w = full_frames[0].shape[:-1] + if args.inference_mode == "wav2clip_only": + out = cv2.VideoWriter("temp/result.avi", cv2.VideoWriter_fourcc(*"DIVX"), fps, (frame_w, frame_h)) + else: + out = cv2.VideoWriter( + "temp/result.avi", + cv2.VideoWriter_fourcc(*"DIVX"), + fps, + (frame_w * args.upscale, frame_h * args.upscale), + ) + + img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(device) + mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).to(device) + + with torch.no_grad(): + pred = model(mel_batch, img_batch) + + pred = pred.cpu().numpy().transpose(0, 2, 3, 1) * 255.0 + + for p, f, c in tqdm(zip(pred, frames, coords), total=pred.shape[0]): + y1, y2, x1, x2 = c + p = cv2.resize(p.astype(np.uint8), (x2 - x1, y2 - y1)) + f[y1:y2, x1:x2] = p # patching + + # restore faces and background if necessary + if args.inference_mode == "wav2clip+gfpgan": + cropped_faces, restored_faces, f = model_restorer.enhance( + f, has_aligned=args.aligned, only_center_face=args.only_center_face, paste_back=True + ) + out.write(f) + out.release() + + # command = "ffmpeg -y -i {} -i {} -strict -2 -c:v libx264 -crf 23 -preset medium -c:a aac {}".format( + # args.audio, "temp/result.avi", args.outfile + # ) + # subprocess.call(command, shell=platform.system() != "Windows") + + ffmpeg.output( + ffmpeg.input(args.audio), + ffmpeg.input("temp/result.avi"), + args.outfile, + strict="-2", + crf=23, + vcodec="libx264", + preset="medium", + acodec="aac", + ).run(overwrite_output=True) + + statistics_dict["opea_service@animation"].append_latency(time.time() - start, None) + # return_str = f"Video generated successfully, check {args.outfile} for the result." + return AnimationDoc(video_save_path=args.outfile) + + +if __name__ == "__main__": + print("Animation initialized.") + opea_microservices["opea_service@animation"].start() diff --git a/comps/animation/assets/audio/sample_question.json b/comps/animation/assets/audio/sample_question.json new file mode 100644 index 0000000000..3ce256e089 --- /dev/null +++ b/comps/animation/assets/audio/sample_question.json @@ -0,0 +1,3 @@ +{ + "byte_str": 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0y/Sh9KH0ffR99Gf0Z/Re9F70XfRd9HD0cPSU9JT0xfTF9Aj1CPVZ9Vn1svWy9R32HfaZ9pn2JPck98T3xPd2+Hb4NPk0+f75/vnV+tX6tfu1+5/8n/yU/ZT9kv6S/pb/lv+hAKEAswGzAccCxwLeA94D+gT6BBIGEgYtBy0HTAhMCGUJZQl9Cn0KlQuVC6AMoAyhDaENnQ6dDocPhw9mEGYQQRFBEQcSBxK9Er0SbBNsEwEUARSFFIUUBBUEFWsVaxXCFcIVERYRFkMWQxZgFmAWchZyFmkWaRZOFk4WKhYqFu0V7RWcFZwVQRVBFc8UzxRMFEwUxxPHEzATMBONEo0S5hHmES0RLRFnEGcQoA+gD9AO0A77DfsNKQ0pDU8MTwxvC28LjwqPCqsJqwnHCMcI6gfqBw4HDgc0BjQGXQVdBYgEiAS2A7YD6wLrAigCKAJtAW0BuQC5AA4ADgBq/2r/zP7M/jr+Ov6w/bD9Lv0u/bT8tPxB/EH80fvR+2j7aPsF+wX7pvqm+k/6T/oA+gD6tPm0+W/5b/kv+S/57/jv+LX4tfh/+H/4SPhI+Bj4GPjr9+v3uve69433jfdg92D3Mfcx9wj3CPfi9uL2u/a79pr2mvZ49nj2UvZS9jD2MPYR9hH28fXx9dj12PXD9cP1rfWt9aD1oPWZ9Zn1lvWW9Z71nvWv9a/1wfXB9d313fUA9gD2JfYl9lb2VvaP9o/2zfbN9hT3FPdi92L3tPe09w/4D/h1+HX44vji+Fr5Wvnc+dz5Y/pj+vL68vqI+4j7Ifwh/L38vfxc/Vz9+/37/Zn+mf45/zn/2v/a/3kAeQAZARkBuAG4AVUCVQLxAvECjQONAyIEIgSxBLEEPAU8Bb4FvgU5BjkGsQaxBh8HHweDB4MH4AfgBy0ILQhuCG4IqAioCNUI1Qj4CPgIEwkTCSAJIAkgCSAJGQkZCQcJBwnrCOsIygjKCJwInAhiCGIIIQghCNUH1QeCB4IHKwcrB8sGywZgBmAG7AXsBW8FbwXrBOsEZQRlBN0D3QNTA1MDxwLHAjgCOAKnAacBFwEXAYwAjAAFAAUAgf+B/wD/AP99/n3++f35/Xj9eP35/Pn8fvx+/An8CfyY+5j7Kvsq+8T6xPpm+mb6D/oP+sT5xPl/+X/5PPk8+f74/vjD+MP4i/iL+GH4YfhC+EL4Kvgq+Bz4HPgU+BT4DPgM+Av4C/gQ+BD4FvgW+CT4JPg2+Db4RvhG+F74Xvh9+H34ovii+NP40/gJ+Qn5QPlA+Xr5evm3+bf59/n3+T76PvqM+oz62/rb+ir7Kvt4+3j7xPvE+xP8E/xn/Gf8vfy9/BT9FP1q/Wr9vf29/RH+Ef5o/mj+w/7D/iH/If9+/37/2v/a/zIAMgCKAIoA5ADkAD4BPgGWAZYB6gHqATcCNwJ/An8CyALIAg8DDwNUA1QDlwOXA9AD0AMDBAMEMwQzBGEEYQSOBI4EuwS7BOEE4QT+BP4EFQUVBSUFJQUwBTAFOQU5BTcFNwUoBSgFDQUNBeUE5QS1BLUEgwSDBE4ETgQRBBEEzQPNA34DfgMlAyUDygLKAm4CbgIRAhECsQGxAUwBTAHiAOIAdgB2AAoACgCh/6H/Of85/9P+0/5r/mv+Av4C/p39nf09/T395Pzk/JP8k/xK/Er8BfwF/Mn7yfuW+5b7bPts+077Tvs6+zr7K/sr+yP7I/sg+yD7Ifsh+yj7KPsz+zP7P/s/+077Tvtc+1z7Zvtm+3D7cPt4+3j7evt6+3v7e/t4+3j7bvtu+2H7YftK+0r7Jvsm+/b69vq3+rf6Zfpl+gj6CPqd+Z35H/kf+Zf4l/gA+AD4WPdY96z2rPb59fn1PfU99YX0hfTL88vzCfMJ80vyS/KO8Y7xzPDM8BbwFvBk72Tvse6x7g7uDu517XXt4+zj7GrsauwI7Ajssuuy63zrfOte617rUetR62jraOuf65/r7evt62DsYOzz7PPsnO2c7WfuZ+5R71HvVPBU8Hfxd/G18rXyB/QH9G71bvXr9uv2e/h7+B36HfrU+9T7m/2b/Wv/a/9HAUcBLwMvAxoFGgUNBw0HBgkGCfMK8wraDNoMvA68DogQiBBDEkMS9RP1E4cVhxX/Fv8WahhqGLMZsxnmGuYaERwRHBcdFx0AHgAe2B7YHoQfhB8RIBEgkiCSIOog6iAhISEhQyFDITUhNSEDIQMhwiDCIF0gXSDeH94fUR9RH58enx7RHdEd+hz6HAwcDBwSGxIbFhoWGgcZBxnmF+YXwhbCFpEVkRVdFF0UNBM0EwYSBhLUENQQpA+kD24Obg46DToNEwwTDPUK9QrcCdwJywjLCLwHvAexBrEGtQW1BcgEyATsA+wDHQMdA1cCVwKVAZUB2gDaACsAKwCH/4f/7f7t/lz+XP7N/c39PP08/a38rfwg/CD8lPuU+w77DvuK+or6BfoF+oT5hPkF+QX5hviG+Az4DPiP94/3CvcK94T2hPb99f31cvVy9e/07/Ru9G706PPo82bzZvPi8uLyW/Jb8uDx4PFv8W/xAfEB8aHwofBJ8Enw8u/y76/vr+9+737vWe9Z703vTe9V71XvZe9l743vje/J78nvFvAW8H/wf/D/8P/wifGJ8SbyJvLV8tXykfOR82f0Z/RW9Vb1VvZW9mj3aPeH+If4sfmx+eb65voq/Cr8ev16/dD+0P4oACgAfQF9AckCyQISBBIEWwVbBZ0GnQbXB9cHBwkHCSAKIAolCyULHwwfDAQNBA3YDdgNoQ6hDksPSw/cD9wPXBBcEMAQwBAPEQ8RUhFSEXgReBGBEYERdRF1EUcRRxH/EP8QqRCpEDoQOhC2D7YPJA8kD3wOfA7DDcMNBA0EDTkMOQxiC2ILhQqFCpoJmgmmCKYIrgeuB7QGtAa4BbgFugS6BLgDuAOwArACpQGlAZ4AngCb/5v/n/6f/q39rf3D/MP83vve+wf7B/s9+j36fvl++dH40fgv+C/4kfeR9//2//Z29nb29PX09YH1gfUa9Rr1t/S39GH0YfQT9BP0yvPK85LzkvNm82bzPvM+8yTzJPMT8xPzBvMG8wnzCfMb8xvzM/Mz81rzWvOG84bzsPOw8+Dz4PMU9BT0RvRG9IH0gfS/9L/0+/T79Dz1PPWC9YL1yfXJ9Rr2GvZy9nL2zfbN9jD3MPeZ95n3A/gD+HX4dfjr+Ov4Yflh+dj52PlP+k/6w/rD+jn7Ofux+7H7Kvwq/Kb8pvwl/SX9pP2k/Sf+J/6t/q3+Nv82/8D/wP9JAEkA0gDSAFgBWAHeAd4BZwJnAu0C7QJxA3ED8APwA2QEZATRBNEEOgU6BZoFmgXzBfMFRAZEBoYGhga7BrsG6QbpBg4HDgctBy0HSgdKB1oHWgddB10HVQdVBz4HPgcdBx0H9Ab0BsAGwAZ+Bn4GMAYwBtMF0wVqBWoF/AT8BIYEhgQIBAgEgwODA/YC9gJjAmMC0QHRAUIBQgG3ALcALwAvAKj/qP8f/x//lf6V/hD+EP6O/Y79D/0P/ZX8lfwg/CD8rfut+0X7Rfvm+ub6kPqQ+kT6RPoA+gD6w/nD+ZP5k/lz+XP5X/lf+V35Xfll+WX5c/lz+Yr5ivmq+ar50fnR+QH6Afo0+jT6ZPpk+pH6kfq++r767Prs+iP7I/ti+2L7pPuk++f75/sq/Cr8bfxt/Lb8tvwF/QX9V/1X/ab9pv3v/e/9Lv4u/mn+af6j/qP+3f7d/hf/F/9M/0z/eP94/53/nf/C/8L/7v/u/yQAJABhAGEAogCiAN8A3wAZARkBUwFTAZABkAHQAdABEgISAk4CTgKEAoQCtQK1AuQC5AIUAxQDRQNFA28DbwOMA4wDmgOaA5gDmAOMA4wDeQN5A14DXgM2AzYDAAMAA7cCtwJeAl4C+QH5AYoBigEKAQoBcwBzAML/wv/y/vL+B/4H/gr9Cv37+/v72vra+q35rflx+HH4KPco9+L14vWh9KH0YfNh8y7yLvL/8P/wy+/L76juqO6R7ZHtguyC7JDrkOuy6rLq3Onc6STpJOmG6Ibo++f7557nnudq52rnU+dT527nbuey57LnFegV6Kbopuhg6WDpNeo16jTrNOtY7Fjsmu2a7QTvBO+S8JLwO/I78gD0APTd9d31zPfM98n5yfnU+9T75f3l/fX/9f8FAgUCFwQXBCMGIwYwCDAIQQpBCkMMQww7DjsOLRAtEAUSBRLLE8sThxWHFR0XHReTGJMY8hnyGSEbIRsuHC4cKx0rHQIeAh69Hr0eah9qH+0f7R9SIFIgqiCqIN4g3iD5IPkgCiEKIfYg9iDIIMggkSCRIDsgOyDRH9EfZB9kH90e3R5EHkQeqh2qHfoc+hw+HD4chhuGG70avRrrGesZGxkbGTwYPBhVF1UXchZyFoIVghWLFIsUlxOXE5wSnBKgEaARqxCrELIPsg+zDrMOsA2wDaAMoAyCC4ILXwpfCjQJNAkBCAEIyAbIBocFhwU+BD4E8ALwAqMBowFWAFYACP8I/7z9vP1s/Gz8FvsW+8L5wvlw+HD4HPcc99H10fWK9Ir0P/M/8//x//HJ8Mnwme+Z74Pug+6A7YDtheyF7Kbrpuva6trqHOoc6oDpgOkC6QLpleiV6EzoTOgf6B/oAugC6AvoC+g36Dfoe+h76OXo5eht6W3pBOoE6rjquOqG64brZexl7GTtZO197n3upu+m7+Tw5PA08jTykPOQ8/z0/PR29nb29/f39335ffkG+wb7jPyM/A3+Df6K/4r/AAEAAWgCaALDA8MDDwUPBUYGRgZrB2sHggiCCIUJhQl2CnYKWwtbCygMKAzhDOEMjg2ODSIOIg6kDqQOGg8aD3gPeA/CD8IP/w//DyQQJBA3EDcQQhBCEDoQOhAmECYQDRANEOMP4w+uD64PdQ91Dy8PLw/iDuIOlw6XDkMOQw7rDesNlQ2VDTcNNw3VDNUMcwxzDAgMCAyVC5ULGwsbC5MKkwr+Cf4JYQlhCbgIuAgHCAcIUQdRB5QGlAbQBdAFBwUHBTcENwRcA1wDdwJ3AogBiAGMAIwAhf+F/3f+d/5e/V79Ofw5/BD7EPvf+d/5pfil+G/3b/c59jn2A/UD9drz2vO78rvyovGi8aDwoPCw77Dvx+7H7vbt9u027Tbtfux+7OPr4+te617r6erp6pPqk+pY6ljqLOos6iHqIeoz6jPqWepZ6qDqoOoC6wLrc+tz6wDsAOyo7KjsYe1h7TbuNu4i7yLvGfAZ8CDxIPE08jTyTfNN83D0cPSb9Zv1xPbE9u337fcU+RT5Nvo2+lX7Vftx/HH8h/2H/ZT+lP6W/5b/kACQAH4BfgFkAmQCQgNCAxAEEATOBM4EfgV+BRwGHAauBq4GNwc3B7EHsQcbCBsIeQh5CMIIwgj+CP4INAk0CV8JXwmBCYEJnwmfCbEJsQm8CbwJyQnJCdEJ0QnVCdUJ1AnUCcUJxQmrCasJkAmQCXAJcAlSCVIJNwk3CRUJFQnoCOgItQi1CHcIdwg0CDQI7gfuB58HnwdDB0MH2wbbBmYGZgbqBeoFbwVvBfIE8gRyBHIE7APsA10DXQPEAsQCJgImAogBiAHoAOgAQwBDAJf/l//h/uH+Iv4i/mH9Yf2d/J381fvV+w/7D/tG+kb6e/l7+bn4ufgB+AH4T/dP9632rfYV9hX2g/WD9QP1A/WW9Jb0NvQ29O7z7vO387fzivOK83DzcPNq82rzdfN185vzm/PV89XzHPQc9HX0dfTg9OD0WvVa9er16vWL9ov2Nfc19+j36Pej+KP4Zfll+TX6NfoT+xP7+Pv4+9z83Py5/bn9iP6I/kr/Sv//////pQClADMBMwGgAaAB6AHoAQoCCgIMAgwC9gH2AcoBygGGAYYBIgEiAZ0AnQD2//b/L/8v/1D+UP5U/VT9Nfw1/PT69PqM+Yz5//f/92f2Z/bJ9Mn0KvMq85vxm/EV8BXwku6S7iftJ+3V69Xrl+qX6oPpg+mR6JHos+ez5//m/+Zz5nPmCuYK5t7l3uXq5erlH+Yf5ozmjOYp5ynn6ufq5+Do4OgG6gbqUOtQ68HswexS7lLu9+/377bxtvGS85LzhvWG9ZP3k/e3+bf55/vn+xn+Gf5LAEsAeQJ5ApcElwSlBqUGoAigCHgKeAozDDMM2Q3ZDV0PXQ/NEM0QMRIxEngTeBOoFKgUzBXMFdAW0Ba+F74XoxijGGgZaBkXGhcauhq6Gj0bPRutG60bHRwdHHocehzRHNEcMB0wHX8dfx3KHcodIR4hHm0ebR65HrkeER8RH1gfWB+WH5Yf2R/ZHwogCiAzIDMgZiBmIIMggyCTIJMgnyCfIIwgjCBjIGMgNCA0IOQf5B92H3Yf9h72HkoeSh53HXcdjRyNHHsbextFGkUa+Bj4GIYXhhfzFfMVThROFJESkRK+EL4Q3A7cDuQM5AzUCtQKsgiyCIIGggZFBEUEAgICAsD/wP99/X39Pvs++xP5E/n99v32/vT+9CbzJvNr8WvxyO/I70zuTO7z7PPstuu266vqq+rI6cjpAekB6Wnoaej65/rnrOes55TnlOep56nn2ufa5zPoM+io6KjoLOks6c/pz+mN6o3qWOtY6zfsN+wi7SLtC+4L7v/u/+7+7/7vAPEA8QvyC/IW8xbzFPQU9Ab1BvXs9ez1wPbA9ov3i/dK+Er49/j3+JX5lfkk+iT6p/qn+ib7Jvum+6b7KPwo/K38rfwz/TP9uv26/UX+Rf7Y/tj+cv9y/xAAEACzALMAWgFaAQQCBAK6AroCfwN/A08ETwQtBS0FGAYYBgYHBgf+B/4HAwkDCQ0KDQocCxwLMwwzDD4NPg1CDkIORA9EDzYQNhAcERwR+RH5EbYSthJXE1cT4hPiE0cURxSOFI4UvRS9FMEUwRSZFJkUTRRNFNAT0BMqEyoTZhJmEnoRehFpEGkQOA84D+QN5A1xDHEM6wrrClMJUwmnB6cH7AXsBSUEJQRTAlMCfgB+ALH+sf7u/O78M/sz+4r5ivnv9+/3Y/Zj9vf09/Sq86rzdvJ28mnxafF88HzwqO+o7/7u/u567nruE+4T7tXt1e237bftre2t7cXtxe397f3tTO5M7rruuu4+7z7vye/J72TwZPAK8Qrxt/G38XLycvI08zTz8vPy86z0rPRh9WH1DPYM9rb2tvZe9173/Pf894/4j/gV+RX5i/mL+fr5+vlj+mP6xfrF+iD7IPtw+3D7tfu1+/j7+PtB/EH8k/yT/O/87/xS/VL9s/2z/Rb+Fv5//n/+9f71/nz/fP8SABIAsACwAE0BTQHpAekBigKKAjQDNAPtA+0DtQS1BIAFgAVKBkoGFQcVB90H3QeoCKgIeAl4CT4KPgrxCvEKjwuPCwwMDAxuDG4MwQzBDPwM/AwiDSINMQ0xDR0NHQ3pDOkMnQydDDYMNgy1C7ULHwsfC2oKagqYCZgJsQixCLgHuAevBq8GnQWdBYEEgQRYA1gDJwInAvQA9ADD/8P/mP6Y/nr9ev1m/Gb8XPtc+2X6Zfp/+X/5qfip+Or36vc+9z73n/af9hn2Gfao9aj1SvVK9QX1BfXV9NX0sfSx9KT0pPSt9K30xfTF9PX09fQz9TP1cPVw9a/1r/Xq9er1H/Yf9ln2WfaW9pb2z/bP9gf3B/c39zf3W/db9333ffee9573vPe899z33Pf79/v3E/gT+C74LvhQ+FD4efh5+K74rvju+O74M/kz+YH5gfna+dr5Pvo++rL6svo2+zb7xPvE+138XfwA/QD9rf2t/WL+Yv4f/x//5P/k/6wArAB7AXsBUwJTAjIDMgMXBBcE+wT7BM4FzgWFBoUGGAcYB3wHfAevB68HsgeyB4EHgQcdBx0HjQaNBtcF1wX/BP8ECAQIBPEC8QKxAbEBQABAAKH+of7M/Mz8xPrE+pn4mfhI9kj21PPU81jxWPHZ7tnuYexh7BTqFOrv5+/n6OXo5RTkFORj4mPixODE4FjfWN8Y3hje/Nz83CTcJNyN243bK9sr2yHbIdtu227bAdwB3O7c7twk3iTehd+F3xnhGeHX4tfiruSu5K7mrubU6NToEOsQ62jtaO3Z79nvW/Jb8vH08fSb95v3S/pL+vP88/yL/4v/CwILAmoEagSvBq8G3QjdCOYK5grPDM8Mmw6bDj8QPxDLEcsRUBNQE8AUwBQkFiQWhReFF8kYyRj+Gf4ZNhs2G10cXRyAHYAdqh6qHrsfux/BIMEg0SHRIdMi0yLdI90j/iT+JBEmESYfJx8nNSg1KC8pLykeKh4qESsRK90r3SuILIgsIS0hLYItgi29Lb0t7C3sLest6y2/Lb8tdi12LeYs5iwZLBksJSslK/Ip8imFKIUo7SbtJg0lDSXoIugilCCUIAkeCR5RG1EbgBiAGJEVkRWFEoUSbg9uD1IMUgw4CTgJKwYrBjIDMgNDAEMAYP1g/ZX6lfrh9+H3SPVI9d3y3fKd8J3wge6B7qDsoOz56vnqiemJ6WfoZ+iI54jn2ObY5mPmY+YZ5hnm6uXq5ejl6OUJ5gnmPOY85pLmkuYC5wLngeeB5yHoIejb6NvonOmc6Wrqauo26zbr7Ovs65vsm+w+7T7tzu3O7VjuWO7X7tfuRe9F77Lvsu8g8CDwi/CL8ALxAvF+8X7x9fH18XHycfLy8vLyd/N38wz0DPS09LT0afVp9TH2MfYR9xH3CvgK+CX5Jflk+mT6w/vD+zr9Ov3L/sv+cwBzACwCLAL7A/sD3wXfBcUHxQetCa0JlQuVC20NbQ06DzoPAREBEaoSqhI1FDUUpxWnFekW6RYFGAUYCRkJGd0Z3RmCGoIa/Br8GjMbMxsrGysb9hr2GocahxrlGeUZHxkfGScYJxgEFwQXxxXHFWkUaRTwEvASZRFlEcAPwA8BDgEONAw0DFwKXAp/CH8IqQapBt4E3gQjAyMDfAF8AfP/8/+H/of+Ov06/Qv8C/z0+vT66/nr+ff49/gU+BT4P/c/94D2gPbS9dL1LvUu9Zz0nPQY9Bj0n/Of8znzOfPd8t3yfvJ+8iTyJPLI8cjxZfFl8Q7xDvG+8L7wbPBs8CPwI/DX79fvge+B7zDvMO/j7uPulO6U7lPuU+4Z7hnu3u3e7bPts+2Y7Zjtiu2K7ZztnO3N7c3tEu4S7nfud+757vnulO+U71fwV/BF8UXxV/JX8pLzkvPt9O30XfZd9t/33/dy+XL5EfsR+7j8uPxk/mT+DgAOAKwBrAE/Az8DzATMBEoGSga/B78HKAkoCXIKcgqbC5sLqAyoDI0NjQ1VDlUOBw8HD5MPkw/5D/kPPRA9EFUQVRBJEEkQJxAnEOMP4w96D3oP8Q7xDkEOQQ50DXQNnQydDMALwAvfCt8K/Qn9CREJEQkcCBwIKwcrB0EGQQZlBWUFlgSWBM0DzQMIAwgDSAJIApQBlAHwAPAAWABYAMj/yP82/zb/n/6f/g3+Df6F/YX9Dv0O/aj8qPxI/Ej85/vn+4X7hfsl+yX7yfrJ+nT6dPoc+hz6tPm0+T75Pvm6+Lr4Lvgu+Kz3rPcx9zH3svay9jD2MPai9aL1CfUJ9Xf0d/Tw8/DzcPNw8wDzAPOd8p3yRfJF8gvyC/L18fXx+vH68R7yHvJO8k7ye/J78qzyrPLh8uHyGvMa82TzZPO787vzGfQZ9Ij0iPQO9Q71r/Wv9XL2cvZX91f3UvhS+GH5YfmE+oT6t/u3+/j8+PxD/kP+j/+P/9AA0AADAgMCLAMsA0UERQRRBVEFUAZQBjQHNAcACAAIuwi7CGQJZAkECgQKnQqdCiILIguPC48L6AvoCyYMJgxMDEwMWwxbDEAMQAzuC+4LXQtdC4cKhwp0CXQJMwgzCM4GzgZGBUYFmAOYA8YBxgHH/8f/nf2d/Vb7Vvvp+On4U/ZT9qTzpPPU8NTw6O3o7QnrCetA6EDoluWW5TPjM+MT4RPhJt8m34TdhN0V3BXcv9q/2pvZm9mZ2JnYqNeo1/DW8NZs1mzWE9YT1hHWEdZn1mfWCtcK1xnYGdiB2YHZHtse2/Hc8dzl3uXe4+Dj4ADjAONA5UDlmOeY5xDqEOqg7KDsOO84797x3vGU9JT0VvdW9xf6F/rQ/ND8bf9t/+MB4wE8BDwEhgaGBr4IvgjyCvIKJA0kDUEPQQ9UEVQRbBNsE3kVeRWHF4cXmxmbGZMbkxt3HXcdWx9bHychJyHyIvIiyiTKJIMmgyYkKCQovim+KTIrMiuZLJksDC4MLlwvXC+QMJAwuDG4MacypzJxM3EzMjQyNLc0tzQENQQ1JDUkNek06TRmNGY0vjO+M9Iy0jKnMacxTTBNMJoumi6aLJosbypvKg8oDyiHJYcl6CLoIhkgGSAbHRsdAxoDGtcW1xahE6ETchByEEENQQ0LCgsK2wbbBsEDwQPMAMwABf4F/nT7dPsG+Qb5rvau9nr0evRo8mjyffB98M/uz+5Q7VDt8Ovw67/qv+q66brp4+jj6FLoUuj+5/7nyufK57bntuet563nneed55nnmeed553nm+eb56Pno+ep56nnqOeo57vnu+fo5+jnJugm6ILogujt6O3oUulS6b7pvukx6jHqqeqp6j3rPevs6+zrr+yv7JXtle2d7p3uwe/B7wjxCPFw8nDy7PPs8331ffUi9yL32fjZ+KX6pfqJ/In8gf6B/oUAhQCXApcCtwS3BNoG2gYACQAJJgsmCzINMg0hDyEP9RD1EJkSmRIXFBcUdhV2FaIWohadF50XcBhwGAQZBBliGWIZmRmZGZYZlhlhGWEZBhkGGXgYeBjDF8MX/Bb8FhwWHBYrFSsVNRQ1FCsTKxMREhES8BDwEMAPwA+DDoMOQw1DDfgL+AukCqQKUQlRCQQIBAjEBsQGmwWbBYwEjASVA5UDuAK4Au4B7gExATEBeAB4ALz/vP/x/vH+Fv4W/jL9Mv1F/EX8UftR+1r6WvpV+VX5N/g3+Ab3BvfA9cD1ZPRk9AbzBvOd8Z3xIfAh8KXupe4o7SjtrOus61PqU+ob6Rvp+ef55wLnAucq5irmbeVt5evk6+Sk5KTkkOSQ5L3kveQf5R/lp+Wn5WjmaOZm52bnleiV6Pvp++mI64jrJO0k7dLu0u6X8JfwcvJy8mz0bPSB9oH2n/if+Lj6uPrJ/Mn80f7R/swAzAC8ArwClwSXBEgGSAbMB8wHKgkqCV8KXwp5C3kLfQx9DFcNVw0BDgEOfg5+DscOxw7sDuwOAA8AD/sO+w7eDt4OsA6wDmkOaQ4cDhwO3A3cDakNqQ2EDYQNbA1sDU8NTw0uDS4NGA0YDQcNBw0EDQQNDQ0NDQ8NDw0HDQcN+gz6DOQM5AzLDMsMsgyyDIgMiAxHDEcM8QvxC4ELgQv/Cv8KdQp1CtgJ2AkfCR8JRwhHCEkHSQcqBioG9QT1BLADsANZAlkC8gDyAID/gP8G/gb+j/yP/Cr7KvvT+dP5hviG+Ef3R/cT9hP27/Tv9PDz8PMZ8xnzY/Jj8tTx1PFb8Vvx7vDu8JnwmfBc8FzwMvAy8CXwJfAs8CzwOfA58FjwWPCI8IjwxPDE8BjxGPF68Xrx1/HX8TTyNPKI8ojyzPLM8gzzDPNG80bzbvNu847zjvOi86LzqfOp87TztPPL88vz7vPu8yv0K/SD9IP08/Tz9IL1gvUy9jL2/fb99uT35Pfn+Of4/vn++Sv7K/tv/G/8yf3J/TX/Nf+0ALQAPwI/AskDyQNNBU0FxQbFBiEIIQhiCWIJjAqMCpQLlAuADIAMUA1QDfMN8w1iDmIOlw6XDoMOgw4fDh8OcA1wDW4MbgwZCxkLegl6CZoHmgd9BX0FLAMsA7EAsQAA/gD+E/sT+/j3+Pen9Kf0J/En8Z7tnu0Q6hDqguaC5iTjJOP+3/7fGt0a3bHasdrF2MXYQtdC1znWOdaG1YbV/dT91LXUtdSg1KDUsNSw1AvVC9Wp1anVc9Zz1oTXhNfW2NbYWtpa2ifcJ9ws3izeRuBG4HDicOKU5JTknuae5qLoouim6qbqqOyo7LPus+7D8MPwz/LP8tv02/Tu9u72BfkF+Rv7G/sp/Sn9Iv8i//sA+wDCAsICiwSLBF0GXQZRCFEIcQpxCqwMrAwNDw0PmxGbET0UPRQCFwIX7RntGdYc1hzGH8YfxCLEIqolqiWEKIQoXiteK/8t/y1uMG4wtzK3Mqs0qzRjNmM2+jf6Nz85Pzk6Ojo6/Tr9OlQ7VDtSO1I7HDscO4s6izqtOa05mTiZOCE3ITdQNVA1RjNGM+ww7DBRLlEukCuQK5AokChaJVolDSINIqkeqR5CG0Ib9Rf1F7wUvBSVEZURiQ6JDpYLlgvCCMIIGAYYBpwDnANIAUgBGP8Y/w79Dv0k+yT7V/lX+bT3tPcy9jL2xPTE9HDzcPMl8iXy2PDY8JTvlO9Q7lDuBu0G7czrzOuj6qPqh+mH6ZTolOjE58TnCOcI527mbubn5eflYeVh5fLk8uSS5JLkOuQ65ATkBOTz4/PjA+QD5E/kT+Ta5Nrkl+WX5ZLmkubC58LnE+kT6YzqjOos7Czs6e3p7cvvy+/T8dPx+PP48zv2O/aa+Jr4DfsN+4r9iv0JAAkAfgJ+AtQE1AQFBwUHDgkOCeEK4QqEDIQMAg4CDlAPUA97EHsQjxGPEXsSexJJE0kT/xP/E4sUixTyFPIUPxU/FWUVZRVsFWwVZRVlFUQVRBUSFRIV3hTeFJkUmRRMFEwU/xP/E6YTphNEE0QT4xLjEnUSdRL/Ef8RjRGNERIREhGWEJYQIhAiEKgPqA8nDycPng6eDv8N/w1EDUQNcAxwDHwLfAtlCmUKMAkwCdkH2QdeBl4GwQTBBAYDBgMnAScBI/8j/wP9A/3G+sb6c/hz+Cn2Kfbv8+/zz/HP8eLv4u8f7h/ufOx87AnrCeu+6b7pkOiQ6Jfnl+fI5sjmFeYV5pHlkeUw5TDl5+Tn5M7kzuTl5OXkJeUl5aPlo+VW5lbmKecp5yHoIegx6THpSepJ6nTrdOuw7LDs8u3y7T7vPu+O8I7w1PHU8RrzGvNj9GP0qPWo9ev26/Yf+B/4Nvk2+TL6Mvoe+x77C/wL/Aj9CP0c/hz+PP88/1cAVwBlAWUBawJrAnIDcgONBI0EwQXBBfsG+wYtCC0ITQlNCVMKUwpOC04LTwxPDEoNSg08DjwOHA8cD9UP1Q92EHYQExETEaoRqhFCEkIS2hLaElITUhOoE6gT5hPmEwQUBBQMFAwUCBQIFN8T3xORE5ETJhMmE5kSmRL0EfQRRhFGEYIQghCnD6cPvQ69DsINwg2/DL8MwAvAC78Kvwq6CboJtgi2CK8HrwerBqsGrwWvBbQEtASxA7EDogKiAogBiAFrAGsAVf9V/1H+Uf5Y/Vj9ZPxk/HT7dPuF+oX6mvma+cP4w/j89/z3QPdA94/2j/bk9eT1PfU99aj0qPQl9CX0qfOp8zzzPPPR8tHyY/Jj8gLyAvKr8avxWfFZ8RXxFfHZ8NnwoPCg8H7wfvB28Hbwg/CD8Kvwq/Df8N/wEPEQ8UbxRvGB8YHxwvHC8RXyFfJ28nby3fLd8lPzU/Pa89rzb/Rv9Br1GvXV9dX1kvaS9lP3U/ca+Br47vju+Nj52Pnb+tv67fvt+wH9Af0Q/hD+E/8T/woACgD5APkA4wHjAb8CvwKQA5ADVQRVBAwFDAW3BbcFVwZXBt4G3gZIB0gHkweTB7sHuwfDB8MHrwevB3QHdAcKBwoHawZrBpMFkwWDBIMERANEA94B3gFLAEsAiP6I/pz8nPx++n76K/gr+LT1tPUL8wvzKvAq8CntKe0I6gjq0ubS5rnjuePG4Mbg/d393Yvbi9ty2XLZqNeo11bWVtZo1WjVuNS41FTUVNQc1BzU89Pz0/7T/tM61DrUmtSa1EDVQNUj1iPWMdcx14bYhtgc2hza2tva28jdyN3N383fx+HH4cbjxuPS5dLl8efx5zzqPOq37LfsUu9S7wXyBfLO9M70pvem94r6ivp6/Xr9aABoADsDOwPwBfAFjgiOCBMLEwugDaANShBKEAETARPRFdEVvhi+GKgbqBubHpseoyGjIZIkkiRsJ2wnOCo4KsQsxCwiLyIvbDFsMXEzcTM9NT013DbcNhM4EzjwOPA4kTmROc85zznCOcI5hjmGOfE48TgTOBM4DjcON8Q1xDVNNE00xTLFMggxCDEeLx4vGi0aLeEq4Sp9KH0oBCYEJmIjYiObIJsgwx3DHdMa0xrbF9sX9BT0FB8SHxJbD1sPrgyuDBEKEQqCB4IHBwUHBacCpwJfAF8AMP4w/hz8HPwb+hv6KPgo+E32TfaB9IH0vPK88grxCvFe717vr+2v7RLsEux+6n7q6ujq6HDncOcO5g7mwuTC5LTjtOPm4ubiUuJS4gziDOIF4gXiJ+In4n/if+IG4wbjreOt44TkhOSF5YXlouai5uvn6+dh6WHp/Or86sXsxey27rbuufC58MjyyPLc9Nz07fbt9v34/fgR+xH7Kv0q/UL/Qv9dAV0BeQN5A4wFjAWWB5YHkgmSCWkLaQsZDRkNoA6gDu8P7w8PEQ8RDhIOEuIS4hKcE5wTThROFOwU7BSCFYIVGhYaFp0WnRYNFw0XchdyF70XvRf3F/cXMBgwGFAYUBhZGFkYTxhPGB0YHRjJF8kXYxdjF9oW2hYvFi8WZRVlFWkUaRQ9Ez0T8BHwEYMQgxD/Dv8OcA1wDdEL0QsiCiIKaQhpCKgGqAbhBOEEGAMYA08BTwGC/4L/r/2v/eH74fsV+hX6TfhN+Jb2lvbq9Or0RvNG87zxvPFK8Erw7u7u7r7tvu217LXszevN6xjrGOuN6o3qIOog6uDp4OnH6cfpzunO6QjqCOpv6m/q8erx6o7rjusu7C7stOy07CjtKO2I7Yjt1e3V7SvuK+6O7o7u+e757nrveu8O8A7wsfCx8HLxcvFR8lHyRPNE80r0SvRa9Vr1afZp9nz3fPeV+JX4tfm1+dn62fr++/77Gv0a/Sr+Kv4w/zD/MAAwACsBKwEmAiYCIgMiAxoEGgQWBRYFIgYiBkEHQQd6CHoIyQnJCQ8LDws9DD0MQw1DDRAOEA6qDqoOIQ8hD3EPcQ+fD58Psw+zD58Pnw9qD2oPJA8kD88Ozw50DnQOIQ4hDskNyQ1zDXMNJQ0lDdkM2QySDJIMUgxSDAsMCwy7C7sLaAtoCw8LDwu3CrcKYQphCv8J/wmFCYUJ7wjvCDsIOwhzB3MHqQapBuIF4gUeBR4FWgRaBJADkAO9Ar0C6gHqAR4BHgFYAFgAl/+X/9f+1/4S/hL+SP1I/YD8gPy3+7f76vrq+h36HfpO+U75hfiF+Nf31/dP90/37/bv9rv2u/aj9qP2mfaZ9qD2oPa49rj24fbh9iL3Ivd193X3zvfO9yv4K/iF+IX40fjR+BH5Efk6+Tr5RflF+Tb5NvkR+RH52fjZ+Jv4m/hY+Fj4CvgK+Lj3uPdj92P3EfcR99L20vas9qz2mvaa9p72nvay9rL20PbQ9gL3AvdN9033sPew9y74LvjE+MT4bvlu+Sv6K/r9+v363vve+8r8yvy8/bz9sv6y/qv/q/+vAK8AwwHDAd0C3QL5A/kDCgUKBQAGAAbWBtYGjAeMBxQIFAhrCGsIjAiMCGsIawgJCAkIbwdvB6AGoAahBaEFegR6BDEDMQPIAcgBRwBHALX+tf4M/Qz9QvtC+1z5XPlT91P3JvUm9e3y7fKd8J3wJe4l7orriuuz6LPok+WT5VziXOIf3x/f7dvt2wfZB9lx1nHWKdQp1GHSYdIW0RbRNNA00NTP1M/Pz8/P8c/xz0rQStDH0MfQV9FX0STSJNIh0yHTMdQx1GTVZNWw1rDWCNgI2JXZldlZ21nbOt063T7fPt9Q4VDhXuNe44jliOXp5+nnj+qP6ovti+3Z8NnwYfRh9A74DvjX+9f7q/+r/28DbwMXBxcHkgqSCscNxw3KEMoQthO2E4kWiRZjGWMZURxRHC8fLx8KIgoi8CTwJLsnuyd6KnoqMi0yLaUvpS/VMdUx0TPRM3E1cTXTNtM2GzgbOBg5GDnQOdA5UDpQOmE6YToROhE6fTl9OYE4gTgwNzA3rDWsNd8z3zPoMegx+S/5LwAuAC4MLAwsKSopKi0oLSgSJhIm4iPiI4YhhiEDHwMfZRxlHJsZmxmkFqQWjhOOE2EQYRAqDSoNAgoCCvIG8gb7A/sDHgEeAWD+YP65+7n7KPko+b32vfZz9HP0SvJK8lnwWfCc7pzuDO0M7bvru+uY6pjqjumO6ajoqOjW59bnCecJ51bmVua55bnlLOUs5c7kzuSl5KXksOSw5AzlDOW35bflnuae5snnyecp6Snpp+qn6kTsROz07fTtpO+k71XxVfEE8wTzq/Sr9FX2VfYH+Af4vfm9+XL7cvsg/SD9wP7A/ksASwDIAcgBQQNBA7gEuAQ5BjkGzQfNB20JbQkgCyAL6AzoDK8Orw5wEHAQJBIkEqoTqhP/FP8UJxYnFg4XDhfAF8AXThhOGKwYrBjsGOwYIRkhGT4ZPhlMGUwZUhlSGTgZOBn5GPkYnhieGBIYEhhdF10XjhaOFpsVmxWHFIcUXRNdExESERKlEKUQIQ8hD38Nfw29C70L4wnjCfAH8AfpBekF3QPdA9oB2gHq/+r/Ff4V/mj8aPzh+uH6fPl8+UH4Qfgm9yb3IPYg9jD1MPVG9Eb0VPNU82PyY/Jr8WvxaPBo8G/vb+997n3ukO2Q7bzsvOwA7ADsVetV683qzepe6l7q++n76bbptumK6Yrpdel16ZPpk+np6enpcOpw6jXrNesr7CvsOe057VnuWe5373fvf/B/8HPxc/FU8lTyKPMo8wT0BPT29Pb0BPYE9jL3Mvd9+H342vna+UP7Q/uz/LP8I/4j/of/h//aANoAEwITAikDKQMgBCAE/wT/BMMFwwV2BnYGHwcfB7wHvAdaCFoIBwkHCb8JvwmGCoYKWgtaCyAMIAzQDNAMaw1rDeUN5Q1GDkYOlg6WDsUOxQ7NDs0Org6uDlwOXA7kDeQNWw1bDccMxww1DDUMsAuwCy8LLwu3CrcKUgpSCgAKAArHCccJqQmpCZYJlgmDCYMJaglqCT0JPQn+CP4IrwivCEsISwjPB88HOwc7B4oGigbABcAF6wTrBBMEEwRAA0ADewJ7AsQBxAEcARwBhwCHAAoACgCq/6r/Zv9m/zv/O/8e/x7/Av8C/93+3f6n/qf+X/5f/gf+B/6j/aP9Mf0x/bj8uPw3/Df8s/uz+zf7N/vJ+sn6bfpt+iz6LPoF+gX68Pnw+e357fnz+fP59/n3+fr5+vn2+fb55vnm+c35zfmi+aL5Xfld+QH5AfmO+I74CvgK+Iz3jPce9x73xvbG9on2ifZd9l32N/Y39hz2HPYQ9hD2GPYY9kD2QPaA9oD2xfbF9gj3CPdB90H3b/dv96f3p/f19/X3W/hb+Nj42Phk+WT59Pn0+Y76jvo6+zr7APwA/OD84PzX/df92P7Y/tb/1v/PAM8AxgHGAbcCtwKeA54DcARwBBkFGQWOBY4FzwXPBd4F3gXFBcUFiQWJBScFJwWWBJYE1QPVA+YC5gLQAdABoACgAGX/Zf8c/hz+wPzA/FP7U/vI+cj5G/gb+Fn2WfZz9HP0W/Jb8hXwFfCG7Ybtnuqe6nznfOce5B7kkOCQ4BDdEN2v2a/ZgNaA1s3TzdOn0afRDdAN0CfPJ8/YztjO6M7ozlzPXM8M0AzQ0NDQ0MfRx9Hl0uXSEtQS1GnVadXa1trWTthO2N/Z39mJ24nbOd053QPfA9/e4N7gt+K34qHkoeSn5qfmz+jP6DPrM+vj7ePt2vDa8Bj0GPSW95b3P/s/+/X+9f6mAqYCOgY6BpAJkAmsDKwMlw+XD0sSSxLnFOcUhxeHFx0aHRrEHMQckB+QH2AiYCI/JT8lMigyKP0q/SqfLZ8tHDAcMD8yPzIZNBk0xTXFNRg3GDclOCU4BTkFOYY5hjmxObE5mDmYOQg5CDkGOAY4rjauNuY05jTOMs4ynjCeMFYuVi4XLBcsBSoFKgMoAygNJg0mJiQmJCYiJiIAIAAgth22HTAbMBtoGGgYaRVpFTMSMxLSDtIOYQthC/cH9wejBKMEdgF2AX3+ff6v+6/7BPkE+YD2gPYZ9Bn0zvHO8bfvt+/b7dvtOOw47Ofq5+rd6d3pC+kL6XnoeegX6Bfoz+fP56rnqueY55jnieeJ55DnkOex57Hn8efx53DocOg56TnpSupK6q/rr+tc7VztOu867z3xPfFP80/zU/VT9T33PfcA+QD5lvqW+gP8A/xS/VL9jf6N/rv/u//lAOUACgIKAiMDIwMvBC8ELwUvBRsGGwb/Bv8G6AfoB9cI1wjeCd4JCgsKC1AMUAyyDbINLQ8tD6EQoRAGEgYSUhNSE2UUZRQ5FTkV0BXQFRoWGhYoFigWFRYVFuQV5BWsFawVfRV9FUYVRhUBFQEVrRStFDQUNBSSE5IT0hLSEucR5xHSENIQnA+cD0EOQQ7JDMkMQQtBC6wJrAkKCAoIXQZdBqAEoATPAs8C9AD0ABz/HP9U/VT9pful+x/6H/rF+MX4lfeV95v2m/bR9dH1KfUp9aT0pPQp9Cn0ofOh8wjzCPNO8k7ybfFt8XXwdfBq72rvUO5Q7kftR+1W7FbsgeuB6+Hq4epw6nDqIOog6v7p/ukB6gHqJOok6oDqgOob6xvr9+v36yLtIu2T7pPuM/Az8PTx9PG587nzY/Vj9ef25/Y/+D/4bflt+YP6g/qS+5L7qfyp/Mz9zP0B/wH/RQBFAJEBkQHjAuMCNgQ2BHkFeQWhBqEGqAeoB3wIfAgjCSMJpQmlCQMKAwpFCkUKcwpzCoMKgwp4CngKWgpaCioKKgr0CfQJyQnJCagJqAmVCZUJkQmRCYsJiwl/CX8JbQltCU4JTgkoCSgJBwkHCecI5wjHCMcIpAikCG4IbggeCB4Itwe3Bz0HPQe9Br0GSgZKBukF6QWjBaMFgQWBBX8FfwWhBaEF5wXnBUEGQQanBqcGDgcOB2kHaQe3B7cH/Qf9BzEIMQhSCFIIWQhZCDkIOQj0B/QHlAeUByAHIAejBqMGHwYfBowFjAXnBOcEMgQyBHcDdwPBAsECGgIaAoABgAHpAOkASwBLAKP/o//1/vX+Sv5K/qv9q/0S/RL9dfx1/NH70fsl+yX7evp6+ub55vl5+Xn5Ovk6+S75LvlO+U75kPmQ+ez57PlT+lP6s/qz+gD7APss+yz7Mvsy+xz7HPv0+vT6wvrC+pD6kPpY+lj6E/oT+sL5wvlj+WP59vj2+In4ifgg+CD4vve+93H3cfc59zn3E/cT9wT3BPcC9wL3//b/9v32/fb79vv29/b39v32/fYN9w33I/cj90P3Q/dw93D3rfet9wv4C/iW+Jb4T/lP+TT6NPo9+z37W/xb/IL9gv2t/q3+1P/U/+wA7ADrAesBwALAAlwDXAOzA7MDxgPGA5gDmAMwAzADmAKYAtcB1wHsAOwA2v/a/6n+qf5Y/Vj97Pvs+3P6c/rp+On4SvdK96L1ovXg8+Dz9PH08ebv5u+Z7Znt9Or06gzoDOjU5NTkUuFS4cndyd1X2lfaGdcZ12LUYtRG0kbSxtDG0AnQCdDyz/LPS9BL0A3RDdEI0gjSC9ML0yzULNRf1V/VlNaU1ufX59dK2UrZqNqo2hjcGNyX3ZfdHN8c37vgu+Bp4mniEOQQ5Lvlu+Vx53HnPuk+6UjrSOuw7bDtf/B/8LjzuPNY91j3SPtI+2P/Y/+SA5IDrwevB4MLgwsBDwEPJhImEusU6xR6F3oXARoBGn8cfxwTHxMfzCHMIYAkgCQuJy4n1inWKTwsPCxfLl8uSDBIMMoxyjH6Mvoy/DP8M7E0sTQwNTA1lDWUNa81rzWGNYY1IzUjNVE0UTQPMw8zcDFwMV4vXi/+LP4siyqLKhIoEii8JbwlqSOpI8IhwiEAIAAgYR5hHr4cvhwHGwcbMhkyGR8XHxe9FL0UDhIOEhYPFg/nC+cLpAikCG0FbQVWAlYCbv9u/7/8v/w++j763/ff96f1p/WM84zzkfGR8dDv0O9S7lLuGO0Y7TbsNuyk66TrUOtQ60PrQ+tt623rteu16xfsF+x67HrsxezF7AHtAe057Tnte+177fDt8O2s7qzusu+y7wnxCfGm8qbycPRw9Fj2WPZG+Eb4IPog+tL70vtR/VH9mv6a/rf/t/+7ALsAtwG3AbMCswK5A7kDyATIBNUF1QXeBt4G4gfiB9UI1Qi7CbsJnwqfCnsLewtjDGMMZg1mDXwOfA6sD6wP8hDyEC4SLhJRE1ETSxRLFPkU+RRPFU8VVRVVFQEVARVmFGYUohOiE74SvhLQEdAR7RDtEBAQEBA7DzsPbg5uDpUNlQ2nDKcMpAukC4QKhApKCUoJAggCCLIGsgZgBWAFEwQTBM8CzwKSAZIBWABYACL/Iv/n/ef9pPyk/GD7YPse+h765Pjk+MX3xffF9sX25PXk9Sz1LPWW9Jb0EvQS9J/zn/Mm8ybzj/KP8trx2vH+8P7w/+//7//u/+4M7gzuL+0v7X3sfezv6+/rd+t36yPrI+v46vjq++r76kzrTOv06/Tr7uzu7DnuOe7A78DvYPFg8QjzCPOn9Kf0LfYt9pv3m/fz+PP4L/ov+lL7Uvtb/Fv8TP1M/S3+Lf4K/wr/8f/x/+QA5ADmAeYB8wLzAvsD+wP5BPkE6wXrBckGyQaVB5UHUAhQCO8I7whsCWwJxwnHCfIJ8gnwCfAJygnKCYEJgQkjCSMJwQjBCF8IXwgGCAYIvAe8B3wHfAdGB0YHHwcfBwQHBAf8BvwGDgcOBzMHMwdoB2gHoAegB8YHxgfNB80HrwevB2wHbAcSBxIHtga2BmEGYQYgBiAG+QX5BeMF4wXiBeIF+wX7BSwGLAZ8BnwG7QbtBnQHdAcLCAsIrgiuCEkJSQnXCdcJUwpTCq0KrQriCuIK9wr3CuYK5gq0CrQKZQplCvIJ8gldCV0JsAiwCPMH8wc4BzgHkQaRBv8F/wV/BX8FCQUJBZMEkwQcBBwEqQOpAz4DPgPeAt4CgwKDAiUCJQK/Ab8BVQFVAfAA8ACaAJoAXABcADYANgAgACAAEgASAAYABgD0//T/1//X/6n/qf9n/2f/Dv8O/6b+pv4z/jP+uP24/Tb9Nv2j/KP88vvy+x/7H/sm+ib6CfkJ+d/33/ex9rH2iPWI9Xn0efSD84Pzo/Kj8ubx5vFH8UfxwPDA8GDwYPAm8CbwCvAK8BnwGfBM8Ezwl/CX8ATxBPGS8ZLxQPJA8hfzF/MW9Bb0LPUs9VL2UvZ493j3kviS+J75nvmc+pz6ivuK+2r8avw6/Tr99/33/Z/+n/4t/y3/m/+b/+D/4P/y//L/zv/O/3X/df/m/ub+Iv4i/i79Lv0I/Aj8svqy+kD5QPm597n3IvYi9ob0hvTM8szy1fDV8KHuoe4X7BfsMOkw6RPmE+bB4sHiQ99D39nb2dug2KDYudW51XvTe9P80fzRKdEp0RDRENF10XXRE9IT0ufS59LU09TTwdTB1NDV0NX81vzWNdg12JjZmNkh2yHbvty+3IPeg95j4GPgReJF4jPkM+Qj5iPmBugG6Onp6enS69Lrxu3G7eLv4u9I8kjyDfUN9Tn4OfjG+8b7jv+O/1cDVwP/Bv8GagpqCn0NfQ1HEEcQ3RLdEjoVOhWDF4MX3BncGT4cPhzHHscegCGAITIkMiTLJssmPCk8KUQrRCvnLOcsNy43LhcvFy+kL6QvCDAIMDQwNDBLMEswbzBvMHcwdzBbMFswFjAWMGgvaC9NLk0u4SzhLBkrGSsfKR8pKScpJzklOSVoI2gjzCHMIUUgRSDEHsQeQB1AHY0bjRuYGZgZYRdhF9kU2RQBEgES6w7rDqgLqAtRCFEIDAUMBfsB+wEu/y7/p/yn/GL6YvpD+EP4NPY09jf0N/RI8kjybfBt8Mbuxu5d7V3tN+w37GzrbOv86vzq3Orc6hPrE+uN643rKewp7N/s3+yb7ZvtSu5K7vru+u6z77Pvf/B/8HnxefGw8rDyJvQm9Nr12vW897z3sfmx+Z77nvtu/W79Ef8R/34AfgC5AbkBzwLPAswDzAPGBMYE0QXRBe0G7QYdCB0IXgleCZYKlgq/C78L1QzVDMkNyQ2jDqMObg9uDyEQIRDJEMkQdRF1ERoSGhLBEsESbhNuEwYUBhR8FHwUyRTJFM4UzhSHFIcU/BP8EygTKBMgEiASAREBEdYP1g+zDrMOpQ2lDaUMpQypC6kLqwqrCpwJnAl4CHgIQQdBB/gF+AWjBKMESwNLA/sB+wG8ALwAmP+Y/5X+lf6q/ar9y/zL/O/77/sG+wb7B/oH+vz4/Pjn9+f3zvbO9sj1yPXZ9Nn0BfQF9FjzWPPH8sfyP/I/8r3xvfEv8S/xhfCF8NHv0e8W7xbvWO5Y7rPts+0q7Srtuey57HLscuxT7FPsVexV7I3sjez+7P7spO2k7YzujO6w77Dv+/D78GPyY/LX89fzQPVA9Z32nfbr9+v3J/kn+VX6Vfpz+3P7fPx8/G79bv1J/kn+Dv8O/77/vv9gAGAA+AD4AIcBhwESAhICnwKfAikDKQOxA7EDMwQzBKcEpwQIBQgFVwVXBY4FjgWpBakFpgWmBX8FfwU9BT0F7wTvBKcEpwR+BH4EhQSFBLsEuwQYBRgFiwWLBfkF+QVRBlEGiQaJBpoGmgaKBooGagZqBkQGRAYoBigGHQYdBhsGGwYaBhoGEwYTBv4F/gXkBeQFzwXPBcMFwwXIBcgF4gXiBQwGDAZOBk4GrgauBiYHJgezB7MHTghOCN4I3gheCV4JzQnNCSUKJQppCmkKnAqcCrEKsQqoCqgKigqKClsKWworCisKCAoICukJ6QnGCcYJmwmbCVkJWQkFCQUJqgiqCEgISAjlB+UHiAeIBy4HLgffBt8GpAakBnsGewZkBmQGXQZdBlYGVgZPBk8GSwZLBksGSwZZBlkGeQZ5BqIGogbJBskG5QblBuMG4wa3BrcGXgZeBtQF1AUcBRwFQgRCBFUDVQNcAlwCXwFfAV0AXQBN/03/Lf4t/gD9AP3P+8/7p/qn+p35nfm1+LX46vfq9zv3O/eT9pP24PXg9Sb1JvVl9GX0pPOk8/3y/fJ38nfyFPIU8tzx3PHG8cbxxfHF8eHx4fET8hPyVvJW8rLysvIg8yDzmfOZ8yL0IvS79Lv0XPVc9Qr2Cva89rz2Yvdi9/v3+/eB+IH47/jv+Ev5S/mO+Y75q/mr+aD5oPlj+WP59Pj0+GP4Y/i197X36fbp9gX2Bfb39Pf0svOy80TyRPKl8KXw0u7S7t7s3uyv6q/qK+gr6GXlZeVR4lHi897z3o/bj9s+2D7YHtUe1YDSgNKF0IXQOM84z8vOy84jzyPPANAA0EvRS9HD0sPSMNQw1KbVptUl1yXXpNik2ELaQtry2/Lbmd2Z3U7fTt8W4Rbh8OLw4vfk9+Qi5yLnTulO6W/rb+t27XbtY+9j71bxVvF383fz4/Xj9av4q/jW+9b7Uf9R//UC9QKmBqYGPwo/Co0NjQ2BEIEQHhMeE10VXRVqF2oXdhl2GYYbhhu8HbwdKSApIKMioyIkJSQlpSelJ+Ip4inOK84rYi1iLWsuay71LvUuKy8rLwYvBi+1LrUuai5qLg8uDy6sLawtQy1DLZ8snyy2K7YrliqWKiMpIyl0J3QnsSWxJdYj1iP/If8hUCBQILgeuB46HTod0hvSG1caVxquGK4YyxbLFpYUlhQIEggSLQ8tDw0MDQy2CLYIRgVGBeUB5QGy/rL+zvvO+1D5UPkr9yv3Q/VD9YbzhvPY8djxLPAs8J3une437TftAuwC7BnrGet96n3qKuoq6jHqMeqP6o/qMesx6xHsEewP7Q/tCe4J7vvu++7k7+TvyPDI8MDxwPHd8t3yJ/Qn9Kj1qPVk92T3TflN+VD7UPtR/VH9LP8s/8MAwwAMAgwCEAMQA+QD5AOsBKwEiQWJBYcGhwa3B7cHGgkaCZ4Kngo7DDsM5g3mDX4Pfg/5EPkQUxJTEnQTdBNlFGUUNxU3Fd8V3xVuFm4W9Rb1FmIXYhe2F7YX7hfuF+UX5ReSF5IX9Rb1FgQWBBbRFNEUfBN8ExESERKoEKgQWA9YDx0OHQ74DPgM5QvlC9AK0AqpCakJZghmCAAHAAd5BXkF4wPjA00CTQLGAMYAW/9b/xj+GP7//P/8D/wP/Eb7RvuR+pH62/nb+Rn5Gfk5+Dn4Nvc29yX2JfYN9Q319fP18/Dy8PL88fzxEvES8ULwQvCG74bv1u7W7jzuPO6s7aztGO0Y7ZTslOwk7CTsz+vP67Xrtevg6+DrTuxO7A7tDu0W7hbuUO9Q77LwsvAf8h/yefN587L0svTA9cD1nvae9lz3XPcD+AP4nfid+Df5N/nY+dj5hvqG+kr7Svsn/Cf8Fv0W/Qn+Cf7z/vP+w//D/3AAcAD8APwAbgFuAdEB0QExAjEClwKXAgADAANnA2cDwQPBA/8D/wMUBBQE/wP/A78DvwNfA18D8ALwAoACgAIbAhsCzQHNAZgBmAF8AXwBcwFzAXYBdgGAAYABkQGRAaoBqgHQAdABBQIFAkMCQwKBAoECvAK8AvIC8gIxAzEDigOKAwAEAASPBI8EJQUlBaoFqgUWBhYGcwZzBsoGygYtBy0HogeiBxgIGAiGCIYI6QjpCD0JPQmPCY8J5gnmCTIKMgpkCmQKcgpyClMKUwocChwK6gnqCckJyQm+Cb4JxAnECcUJxQm/Cb8JvAm8CcEJwQnSCdIJ6wnrCfIJ8gnYCdgJoQmhCVEJUQkACQAJwwjDCJYIlgh2CHYIXgheCEQIRAguCC4IKQgpCDUINQhTCFMIfgh+CKIIogi0CLQIsAiwCIgIiAg5CDkIyAfIBzQHNAeKBooG2wXbBS8FLwWJBIkE5QPlAzcDNwN2AnYCowGjAcUAxQDn/+f/Ff8V/1b+Vv6n/af9AP0A/V/8X/y8+7z7FfsV+3P6c/rb+dv5UflR+eP44/iR+JH4WfhZ+D34Pfg1+DX4Nvg2+EL4QvhY+Fj4ePh4+Kr4qvju+O74O/k7+Yz5jPnP+c/59vn2+QH6Afrz+fP50fnR+av5q/mA+YD5RflF+fT49PiA+ID44/fj9yz3LPdc9lz2cPVw9W30bfQ+8z7z0vHS8TrwOvB37nfujuyO7J3qneqK6IroLeYt5ojjiON44Hjg+Nz43FLZUtms1azVKdIp0iLPIs+mzKbMtcq1yonJickhySHJYclhyVnKWcrQy9DLc81zzTTPNM/q0OrQfdJ90iDUINTc1dzVqNeo15fZl9mW25bbjd2N3Zbflt+74bvh+uP6413mXebU6NToROtE67DtsO0v8C/w2vLa8tH10fUo+Sj5z/zP/JwAnABsBGwEHggeCIsLiwuwDrAOjRGNEQ8UDxROFk4WbRhtGHMacxqPHI8c4x7jHlAhUCHVI9UjYyZjJroouijNKs0qmyybLPAt8C3SLtIuWi9aL3Mvcy9EL0QvAi8CL5kumS4bLhsukC2QLcksySzHK8croiqiKkcpRynJJ8knRiZGJqYkpiT7IvsiYiFiIdUf1R9mHmYeHB0cHc0bzRtYGlgapxinGJwWnBYzFDMUgRGBEZEOkQ5vC28LLgguCOQE5ASnAacBmf6Z/t773vt9+X35bvdu96b1pvUC9AL0a/Jr8unw6fB+737vMu4y7iTtJO1S7FLstuu261vrW+s86zzrUOtQ66brpusy7DLs3ezd7KDtoO1o7mjuKu8q7/rv+u/o8OjwAfIB8lDzUPPQ9ND0afZp9gr4Cvij+aP5Kfsp+5f8l/zp/en9GP8Y/yAAIAALAQsB7QHtAdsC2wL3A/cDUAVQBd0G3QaWCJYIagpqCjkMOQz5DfkNqA+oDy0RLRGLEosSyBPIE9QU1BS8FbwVkRaRFkEXQRfOF84XOBg4GF8YXxg8GDwY1hfWFx4XHhciFiIW/hT+FLsTuxN1EnUSSxFLEUAQQBBXD1cPig6KDr4Nvg3fDN8M4wvjC74Kvgp0CXQJEQgRCJ0GnQYkBSQFtwO3A2ICYgIvAS8BIwAjADz/PP9u/m7+pv2m/df81/zw+/D76vrq+s/5z/mi+KL4b/dv9072TvZF9UX1WPRY9JPzk/Ps8uzyU/JT8sfxx/E08TTxjvCO8Ojv6O9N703v0e7R7pvum+607rTuFO8U77TvtO948HjwQvFC8Q7yDvLO8s7yePN48wz0DPR+9H70x/TH9Pb09vQh9SH1YvVi9dn12fWP9o/2ePd49374fviF+YX5c/pz+kD7QPvn++f7aPxo/Mb8xvwG/Qb9Lf0t/Uj9SP1o/Wj9mf2Z/eb95v1O/k7+yf7J/kj/SP+6/7r/EAAQAEAAQABIAEgAMQAxAAwADADw//D/7//v/xEAEQBSAFIAoQChAOkA6QAbARsBNgE2AT8BPwFGAUYBWQFZAX8BfwG4AbgBBQIFAmACYALRAtECXwNfAwgECATKBMoElgWWBVQGVAb1BvUGcwdzB80HzQcRCBEIUQhRCJAIkAjUCNQIJAkkCXUJdQnHCccJHAocCmEKYQqPCo8KoQqhCosKiwpTClMKBwoHCqsJqwlICUgJ6QjpCIsIiwgzCDMI7AfsB7IHsgeHB4cHZwdnB0QHRAccBxwH9gb2BtkG2QbTBtMG8gbyBjAHMAeJB4kH9gf2B2QIZAjPCM8INQk1CYgJiAnGCcYJ7AnsCfAJ8AnOCc4JjQmNCSUJJQmdCJ0I/gf+B0wHTAePBo8G0gXSBRYFFgVVBFUEjgOOA7kCuQLSAdIB4ADgAO7/7v8J/wn/P/4//p39nf0n/Sf92PzY/Kn8qfyJ/In8avxq/Eb8RvwZ/Bn86Pvo+7/7v/ut+637vfu9+/n7+ftj/GP88/zz/Jr9mv1C/kL+1v7W/kf/R/+N/43/rf+t/7D/sP+d/53/dv92/zv/O//r/uv+iP6I/h/+H/63/bf9Uv1S/e787vxw/HD8vPu8+8X6xfp7+Xv55Pfk9x/2H/Y19DX0LvIu8hvwG/Dd7d3tUOtQ63roeug65Trlh+GH4Zvdm92K2YrZctVy1bTRtNFwznDOv8u/y+zJ7Mn8yPzI0sjSyHrJesm1yrXKKMwozLzNvM02zzbPb9Bv0JPRk9Gz0rPS19PX0zXVNdXM1szWhdiF2HLactqF3IXco96j3tbg1uAN4w3jLuUu5UfnR+dl6WXpmuua6w/uD+7k8OTwGvQa9KL3ovdh+2H7L/8v/90C3QJbBlsGmAmYCX8MfwwfDx8PjRGNEdAT0BMdFh0WohiiGFobWhtQHlAecyFzIXIkciQoJygneyl7KTIrMitZLFksEy0TLVMtUy1ELUQtGS0ZLcUsxSxoLGgsHywfLMQrxCtVK1Ur1SrVKhMqEyoOKQ4p2ifaJ2YmZibNJM0kOSM5I6khqSEyIDIg5B7kHpsdmx1AHEAcwBrAGvMY8xjJFskWTRRNFIMRgxF6DnoOUAtQCx0IHQj9BP0EEAIQAnP/c/8w/TD9QvtC+5z5nPkY+Bj4mfaZ9hn1GfWS85LzC/IL8qTwpPBo72jvXe5d7pztnO0p7SntAe0B7S/tL+2i7aLtO+477uru6u6W75bvL/Av8MXwxfBo8WjxJvIm8hXzFfM29Db0efV59dD20PYm+Cb4Yvli+Xf6d/pd+137D/wP/Jj8mPwK/Qr9ev16/QX+Bf7I/sj+1//X/zUBNQHeAt4CvgS+BK4GrgaTCJMIWQpZCuQL5As3DTcNXg5eDlQPVA8sECwQ9xD3EK8RrxFfEl8SCxMLE5kTmRP/E/8TMxQzFBoUGhS2E7YTHRMdE1QSVBJ5EXkRqRCpEOcP5w88DzwPqg6qDhsOGw6CDYIN0wzTDPkL+QvvCu8KvQm9CWoIaggLBwsHvAW8BY8EjwSVA5UD1gLWAkoCSgLdAd0BeQF5AQUBBQFwAHAAtP+0/9v+2/7v/e/9/fz9/Bb8Fvw9+z37b/pv+rL5svn/+P/4UPhQ+Kz3rPcQ9xD3fvZ+9gb2Bvaw9bD1gPWA9YH1gfWw9bD1BPYE9n32ffYR9xH3r/ev90z4TPjL+Mv4FfkV+SD5IPnr+Ov4gfiB+AD4APiE94T3Hvce9+f25/bp9un2J/cn96L3ovdK+Er4/vj++KT5pPkf+h/6Yfph+nj6ePp5+nn6d/p3+oX6hfqg+qD6vvq++t763vr7+vv6FfsV+y/7L/s++z77NPs0+xD7EPvi+uL6yfrJ+u/67/pu+277Q/xD/FH9Uf1t/m3+bv9u/z0APQDdAN0AWQFZAbkBuQH9Af0BIQIhAiICIgIJAgkC7AHsAd8B3wH3AfcBPAI8AqYCpgItAy0DygPKA3IEcgQgBSAFzAXMBWYGZgbmBuYGTQdNB5oHmgfdB90HIwgjCGUIZQicCJwIuQi5CKkIqQhpCGkICAgICJIHkgcfBx8HwwbDBoIGggZcBlwGTgZOBkwGTAZOBk4GUgZSBk4GTgZCBkIGMQYxBhgGGAb6BfoF3wXfBccFxwW8BbwFzAXMBfoF+gVQBlAG0QbRBmwHbAcXCBcIxAjECF0JXQncCdwJPQo9CnUKdQp8CnwKUApQCucJ5wlHCUcJgQiBCKMHowe+Br4G2QXZBesE6wToA+gDxwLHAo0BjQFLAEsAGv8a/xb+Fv5N/U39v/y//Gr8avxC/EL8Ofw5/Er8Svxp/Gn8ivyK/Kf8p/y6/Lr8xvzG/Nv82/wC/QL9Q/1D/Zr9mv0B/gH+bf5t/t/+3/5a/1r/5P/k/3kAeQAJAQkBfQF9Ab4BvgHEAcQBmQGZAVQBVAEGAQYBuQC5AGMAYwDn/+f/K/8r/yX+Jf7M/Mz8LPss+1/5X/lp92n3SPVI9QnzCfOR8JHwyu3K7b/qv+pZ51nnlOOU46jfqN+t263bxNfE10XURdRK0UrR2c7ZzizNLM06zDrM6cvpy1HMUcxHzUfNhM6EzvfP989n0WfRp9Kn0tzT3NMT1RPVUdZR1sDXwNdY2VjZ/Nr82rrcutyQ3pDec+Bz4HzifOKi5KLkyubK5vXo9egq6yrrde117fnv+e/P8s/y8PXw9UP5Q/mn/Kf8+f/5/x0DHQMRBhEG0QjRCEwLTAuHDYcNkw+TD3cRdxFpE2kTnBWcFQsYCxi7GrsakB2QHTogOiCXIpcinSSdJCUmJSZCJ0InEygTKIQohCixKLEovSi9KJgomChdKF0oIigiKMknySdUJ1QnzSbNJhQmFCYyJTIlPSQ9JCQjJCP6Ifoh3CDcIL4fvh+mHqYemR2ZHXEccRwWGxYbfhl+GY4XjhdEFUQVsBKwEtcP1w/JDMkMpAmkCYkGiQaUA5QD5wDnAJT+lP6Y/Jj84Prg+mH5YfkD+AP4tfa19n71fvVY9Fj0P/M/80TyRPJp8WnxsvCy8DfwN/D+7/7vAfAB8ELwQvCw8LDwMfEx8bzxvPFD8kPyvfK98jjzOPPA88DzXPRc9B71HvUB9gH29fb19u737vfa+Nr4rvmu+W76bvoc+xz7ufu5+0X8RfzC/ML8Ov06/cL9wv17/nv+fv9+/88AzwBoAmgCLgQuBPcF9wWuB64HRwlHCbMKswr2C/YLEw0TDfsN+w24DrgOWQ9ZD+EP4Q9dEF0Q1hDWEDARMBFbEVsRSxFLEe0Q7RBJEEkQeQ95D4gOiA6NDY0NnwyfDMILwgv8CvwKVgpWCsIJwgk1CTUJowijCPsH+wc2BzYHWgZaBnAFcAWHBIcEsgOyA/sC+wJnAmcC9QH1AZwBnAFNAU0B+wD7AJkAmQAeAB4Aiv+K/+j+6P4//j/+nP2c/Qr9Cv2M/Iz8IPwg/Mf7x/t6+3r7NPs0+/b69vq/+r/6jPqM+mH6Yfo/+j/6Jvom+iH6Ifo8+jz6g/qD+v76/vqm+6b7Y/xj/Bj9GP2h/aH95f3l/eP94/2h/aH9Lv0u/aD8oPwD/AP8X/tf+8b6xvpE+kT64Png+ab5pvmP+Y/5j/mP+aT5pPnS+dL5I/oj+qr6qvpr+2v7WPxY/FX9Vf07/jv+7v7u/mD/YP+P/4//g/+D/0j/SP/j/uP+YP5g/tL90v1W/Vb9Cf0J/QD9AP1F/UX9zv3O/YX+hf5V/1X/LAAsAPoA+gC0AbQBSwJLArICsgLkAuQC5QLlAsICwgKTApMCbAJsAlgCWAJcAlwCeAJ4AqMCowLbAtsCIQMhA24DbgPCA8IDHAQcBHQEdATMBMwEJAUkBXIFcgWrBasFwQXBBaMFowVJBUkFugS6BAEEAQQ3AzcDcwJzAsUBxQE8ATwB3wDfALEAsQC0ALQA5QDlADkBOQGiAaIBEAIQAnACcAK8ArwC8wLzAhQDFAMpAykDNQM1AzsDOwM/Az8DTQNNA2wDbAOnA6cDAgQCBHIEcgTjBOMEQAVABW4FbgVnBWcFNAU0BeME4wSJBIkENwQ3BO0D7QOgA6ADRANEA8sCywIuAi4CcgFyAaYApgDb/9v/Jf8l/5b+lv46/jr+E/4T/h/+H/5W/lb+rP6s/hT/FP+B/4H/6P/o/0AAQACEAIQAtwC3ANsA2wD4APgAFQEVATcBNwFkAWQBnwGfAeoB6gFEAkQCpQKlAv4C/gI+Az4DWQNZA0oDSgMZAxkD2QLZApkCmQJfAl8CKAIoAugB6AGPAY8BEQERAWUAZQB4/3j/OP44/p38nfyb+pv6Ofg5+KD1oPXg8uDy/u/+7wztDO3j6ePpZeZl5rXiteLf3t/eCNsI243XjdeN1I3UE9IT0ljQWNBaz1rPCs8Kz4HPgc+R0JHQ6dHp0WvTa9PZ1NnUC9YL1ijXKNdA2EDYVNlU2YPag9rB28Hb+tz63FDeUN7T39Pfg+GD4W7jbuN25XblbOds50npSekU6xTr6Ozo7PXu9e5d8V3xGPQY9BT3FPc5+jn6av1q/ZIAkgCtA60DrQatBnQJdAkCDAIMZA5kDqAQoBDpEukSZxVnFQwYDBjaGtoavh2+HXYgdiDtIu0iGCUYJcAmwCbkJ+QnkSiRKLIosihyKHIoDCgMKIgniCcMJwwnsCawJk8mTybkJeQldCV0Jdsk2yQfJB8kTiNOI1AiUCIuIS4hAyADIMYexh6KHYodYBxgHCkbKRvUGdQZVRhVGI0WjRZ6FHoULBIsEqkPqQ/+DP4MPAo8CnEHcQexBLEEFgIWAr7/vv+0/bT99vv2+3j6ePoZ+Rn5uve691X2Vfbm9Ob0dPN08x7yHvLv8O/w7u/u7zLvMu/A7sDumu6a7tPu0+5g72DvJ/An8BPxE/H/8f/xzvLO8n/zf/MY9Bj0pPSk9Db1NvXV9dX1ePZ49iH3IffI98j3ZPhk+Pj4+Ph5+Xn53vne+SX6JfpY+lj6iPqI+tX61fpj+2P7RPxE/Hv9e/3+/v7+twC3AIMCgwJPBE8ECQYJBpoHmgf7CPsIKAooChkLGQvdC90LigyKDCcNJw3FDcUNbA5sDgUPBQ+BD4EP0g/SD9wP3A+dD50PHg8eD2QOZA6EDYQNmwybDLQLtAvgCuAKKQopCoUJhQnsCOwIUwhTCK0HrQf1BvUGLgYuBl0FXQWMBIwEyAPIAxwDHAORApECMQIxAvcB9wHXAdcBuwG7AYoBigEsASwBmACYANP/0//r/uv+7/3v/fT89PwE/AT8I/sj+176Xvq4+bj5NPk0+dj42Pic+Jz4cvhy+Fj4WPhH+Ef4Pvg++E74TviD+IP44vji+HL5cvkp+in69Pr0+r37vftu/G789fz1/E39Tf14/Xj9gf2B/Xr9ev10/XT9e/17/Zv9m/3T/dP9Gv4a/l7+Xv6L/ov+kf6R/nb+dv5M/kz+Mv4y/kX+Rf6Q/pD+CP8I/5T/lP8aABoAigCKAOUA5QA2ATYBhQGFAdAB0AEPAg8COQI5AkoCSgJOAk4CVQJVAmwCbAKbApsC4gLiAjkDOQOdA50DCwQLBHgEeATZBNkEIQUhBUAFQAU1BTUFDgUOBd0E3QS8BLwEugS6BNgE2AQQBRAFVwVXBZwFnAXYBdgFCQYJBisGKwZCBkIGWgZaBnEGcQaOBo4GswazBtUG1QbtBu0G8wbzBt0G3QalBqUGSAZIBsEFwQUSBRIFRQRFBGUDZQOEAoQCsQGxAfQA9ABNAE0Auv+6/zr/Ov/S/tL+iv6K/mr+av5z/nP+nP6c/tX+1f4N/w3/Ov86/1n/Wf90/3T/mP+Y/9H/0f8jACMAhQCFAOgA6AA1ATUBVgFWAT8BPwHrAOsAYgBiALP/s//y/vL+L/4v/n/9f/3p/On8a/xr/AX8Bfym+6b7QvtC+9f61/pn+mf6//n/+bH5sfmJ+Yn5iPmI+bD5sPn6+fr5Yvpi+uv66/qX+5f7X/xf/Dv9O/0b/hv+8f7x/rL/sv9hAGEAAQEBAZUBlQEdAh0CmwKbAggDCANnA2cDvQO9AwIEAgQxBDEEPwQ/BCIEIgTeA94DhAOEAygDKAPcAtwCoAKgAmYCZgIPAg8ChQGFAcAAwAC5/7n/c/5z/vT89Pwr+yv7BvkG+Y72jva487jzjPCM8DbtNu2+6b7pL+Yv5r7ivuJ333ffbNxs3ODZ4Nni1+LXcdZx1q7VrtWC1YLVwtXC1W3WbdZR11HXNtg22BzZHNnm2ebZhNqE2iDbINvI28jbetx63FjdWN1h3mHehd+F3+Dg4OBu4m7iHOQc5Ovl6+XD58PniemJ6UrrSusb7RvtDe8N7z7xPvGv86/zSfZJ9u747viM+4z7GP4Y/o4AjgD8AvwCZwVnBcIHwgcdCh0KjQyNDBkPGQ/oEegRCxULFVYYVhiqG6ob4R7hHq0hrSH+I/4j3CXcJS0nLScIKAgojiiOKLAosCiPKI8oWChYKAIoAiipJ6knZSdlJxEnESeqJqomNiY2Jo8ljyW7JLsk0iPSI8QixCKjIaMhhSCFIFIfUh8FHgUenhyeHP8a/xoiGSIZExcTF80UzRRcElwS1w/XD0YNRg28CrwKVQhVCCMGIwY1BDUEjwKPAioBKgHt/+3/wf7B/pb9lv1a/Fr8CPsI+6v5q/lG+Eb44fbh9pX1lfVr9Gv0bfNt87LysvJA8kDyEfIR8inyKfJ28nby3PLc8lDzUPO/87/zHfQd9HL0cvTB9MH0B/UH9Ur1SvWB9YH1oPWg9bD1sPWz9bP1qvWq9aX1pfWk9aT1pfWl9bb1tvXo9ej1SvZK9vT29Pbu9+73MPkw+af6p/o+/D784P3g/Xn/ef//AP8AZAJkApsDmwOeBJ4EdAV0BSEGIQa9Br0GWgdaB/kH+QecCJwIPgk+Cc8JzwlHCkcKpQqlCtgK2ArcCtwKswqzClYKVgrVCdUJRAlECa4IrggfCB8IngeeByMHIwepBqkGNAY0Br4FvgVNBU0F5gTmBIkEiQQ8BDwEBwQHBO4D7gPyA/IDEgQSBD4EPgRkBGQEdQR1BGMEYwQpBCkEyQPJA0gDSAOrAqsC+gH6ATwBPAF1AHUArf+t/+7+7v4+/j7+pP2k/SP9I/21/LX8T/xP/Oz77PuE+4T7GPsY+7j6uPpv+m/6Q/pD+jn6OfpF+kX6Wvpa+nP6c/qJ+on6mfqZ+qL6ovqW+pb6afpp+hn6Gfqs+az5MPkw+cX4xfh++H74Yvhi+HX4dfit+K34+vj6+Fj5WPm/+b/5I/oj+n/6f/rQ+tD6FfsV+1v7W/uw+7D7Hvwe/Kz8rPxU/VT9C/4L/sf+x/6H/4f/TgBOAB0BHQH4AfgB3QLdAr8DvwOYBJgEZgVmBSEGIQbQBtAGegd6BxoIGgiwCLAIPQk9CbIJsgkOCg4KVwpXCosKiwq3CrcK7gruCi0LLQt6C3oL1wvXCzQMNAyMDIwM4AzgDB4NHg1FDUUNVA1UDT8NPw0GDQYNtAy0DEUMRQzEC8QLPgs+C60KrQoXChcKfQl9CdkI2QgqCCoIcQdxB60GrQbiBeIFGQUZBVUEVQSZA5kD5ALkAjQCNAKJAYkB6gDqAGEAYQD5//n/uf+5/6D/oP+o/6j/xf/F//H/8f8kACQAVQBVAHsAewCIAIgAbwBvACoAKgC1/7X/Fv8W/1n+Wf6D/YP9l/yX/J77nvuY+pj6jfmN+ZH4kfiu96737vbu9l72Xvb89fz1xPXE9bb1tvXK9cr18fXx9Sn2KfZk9mT2lfaV9sD2wPbk9uT2CfcJ9z/3P/eT95P3CPgI+KL4ovhb+Vv5K/or+g77DvsB/AH8Av0C/Q3+Df4V/xX/BwAHAM8AzwBfAV8BtgG2Ad4B3gHkAeQB1wHXAbsBuwGMAYwBQgFCAdoA2gBOAE4AnP+c/8f+x/7G/cb9k/yT/Db7Nvut+a35+vf69y32LfYz9DP09vH28Xrveu+r7KvsjumO6V7mXuY+4z7jUuBS4N/d39302/TbjNqM2svZy9mm2abZA9oD2ufa59ol3CXce9173dbe1t4P4A/gDuEO4fXh9eHN4s3iluOW42fkZ+Q45TjlAeYB5uDm4Obj5+PnC+kL6WLqYurR69HrNu027Y3uje7f79/vPfE98cryyvKQ9JD0gPaA9ob4hviO+o76ifyJ/HP+c/5RAFEAHQIdAsgDyANZBVkF5gbmBoUIhQhqCmoKtgy2DFYPVg87EjsSQRVBFSkYKRjeGt4aVh1WHWMfYx8DIQMhPiI+Iv0i/SJlI2UjqSOpI8sjyyPoI+gjEiQSJCMkIyQeJB4kDyQPJN0j3SOSI5IjNyM3I60irSL2IfYhJiEmITMgMyAxHzEfMx4zHiEdIR3vG+8bmRqZGgMZAxkrFysXIRUhFecS5xKNEI0QMg4yDucL5wvCCcIJ3AfcBzwGPAbeBN4EuAO4A7YCtgK6AboBsACwAI3/jf9I/kj+6vzq/In7ifs3+jf6CfkJ+Rb4Fvho92j3AvcC9+z27PYb9xv3fPd89/z3/Pd/+H/46/jr+Dz5PPlv+W/5ifmJ+Zv5m/mk+aT5ofmh+ZT5lPl5+Xn5SvlK+Q/5D/nB+MH4Wvha+OH34fdg92D36fbp9p32nfaW9pb23/bf9nz3fPdg+GD4dPl0+aH6ofrQ+9D76fzp/Nf91/2P/o/+Ev8S/2v/a/+y/7L//////18AXwDZANkAZwFnAfsB+wGLAosCDAMMA3ADcAOsA6wDuwO7A5gDmANMA0wD6wLrAoUChQIpAikC3QHdAZ4BngFiAWIBIwEjAd0A3QCRAJEARABEAPr/+v+8/7z/l/+X/5b/lv/K/8r/OgA6AOAA4ACsAawBhwKHAk8DTwPtA+0DUQRRBHIEcgRVBFUECAQIBJwDnAMlAyUDtwK3AmACYAImAiYCCwILAgACAAL3AfcB4QHhAbIBsgFoAWgBCQEJAaEAoQA5ADkA1//X/3v/e/8i/yL/x/7H/m7+bv4U/hT+uf25/Vz9XP31/PX8f/x//AD8APx++377BfsF+6b6pvpl+mX6QPpA+jb6Nvo8+jz6RfpF+k76TvpE+kT6GPoY+sj5yPlW+Vb5zvjO+FH4Ufj59/n31PfU9+z37Pc7+Dv4sfix+Ef5R/ny+fL5qPqo+mL7Yvsa/Br8zPzM/Hz9fP00/jT+/v7+/tz/3P/NAM0AyAHIAcECwQK0A7QDogSiBIMFgwVYBlgGIAcgB9IH0gd5CHkIIwkjCdEJ0QmQCpAKYQthCy0MLQzsDOwMmg2aDScOJw6XDpcO7w7vDiEPIQ8wDzAPKg8qDw4PDg/vDu8O4w7jDuEO4Q7nDucO7w7vDuAO4A6wDrAOYg5iDugN6A1GDUYNhAyEDKULpQu2CrYKzQnNCfII8gguCC4IhgeGB/EG8QZrBmsG9QX1BYsFiwUvBS8F4wTjBKIEogRrBGsEPgQ+BBMEEwTkA+QDqQOpA1MDUwPVAtUCJwInAkgBSAE/AD8AFv8W/+L94v2r/Kv8fPt8+2H6Yfpe+V75dvh2+LX3tfcc9xz3pvam9lf2V/Yj9iP2+/X79eD14PXM9cz1vfW99cD1wPXX9df1/fX99TX2NfZ49nj2vva+9g/3D/dr92v30PfQ90D4QPix+LH4Gvka+X/5f/nl+eX5UvpS+tD60PpX+1f71/vX+z/8P/yC/IL8oPyg/KT8pPya/Jr8gPyA/FT8VPwB/AH8c/tz+6z6rPqn+af5avhq+AX3Bfdw9XD1ovOi86zxrPGG74bvMu0y7dLq0upi6GLo3eXd5WnjaeML4Qvh1N7U3gHdAd2l26Xbvdq92mfaZ9qK2ora/9r/2snbydvK3Mrc3N3c3QbfBt814DXgU+FT4XnieeKm46bjz+TP5AfmB+ZL50vnlOiU6Pnp+emA64DrIO0g7dnu2e6Y8JjwRfJF8uHz4fN59Xn1GPcY9874zvib+pv6bvxu/DT+NP7o/+j/jQGNASYDJgO6BLoERQZFBrAHsAf/CP8IRApECo0LjQsADQANtQ61DpQQlBCSEpISmhSaFIEWgRZCGEIY4BngGT0bPRtXHFccNx03HcUdxR0aHhoeVR5VHnAecB5+Hn4ejR6NHoUehR5sHmweUB5QHhkeGR7KHcodbB1sHekc6RxMHEwcrRutGwEbARtOGk4amxmbGcoYyhjbF9sX2hbaFr0VvRWMFIwUTxNPE/MR8xF5EHkQ7w7vDl4NXg3dC90LgwqDClEJUQlCCEIITQdNB2AGYAZvBW8FdQR1BGwDbANOAk4CIQEhAez/7P++/r7+pv2m/bb8tvz6+/r7d/t3+zP7M/so+yj7S/tL+4/7j/vi++L7N/w3/Ir8ivza/Nr8Kf0p/Xr9ev3I/cj9Df4N/kj+SP56/nr+qP6o/tT+1P71/vX+/v7+/uP+4/6k/qT+T/5P/v79/v3H/cf9tf21/cb9xv3v/e/9If4h/lb+Vv6M/oz+vP68/t/+3/7p/un+0v7S/p/+n/5i/mL+MP4w/h3+Hf4w/jD+Yv5i/qX+pf7t/u3+Lf8t/1//X/98/3z/fP98/1j/WP8L/wv/nP6c/hj+GP6O/Y79B/0H/Yz8jPwb/Bv8r/uv+0z7TPv1+vX6rfqt+nz6fPpk+mT6Zfpl+oT6hPrC+sL6IPsg+5/7n/s1/DX8z/zP/Fz9XP3N/c39Gf4Z/kX+Rf5e/l7+cv5y/o7+jv66/rr++f75/kz/TP+w/7D/IQAhAJUAlQADAQMBXQFdAZcBlwGnAacBjAGMAUUBRQHYANgATgBOALT/tP8W/xb/gP6A/v/9//2a/Zr9Uv1S/Sv9K/0i/SL9Nf01/WT9ZP2p/an9+f35/Uf+R/6I/oj+sf6x/sH+wf67/rv+oP6g/nT+dP41/jX+4P3g/Xv9e/0Q/RD9rPys/GL8Yvw+/D78Pvw+/GH8Yfya/Jr83/zf/C39Lf18/Xz9xf3F/QL+Av4o/ij+Mv4y/ij+KP4Y/hj+FP4U/ir+Kv5h/mH+tf61/h//H/+e/57/NAA0AOcA5wC6AboBqgKqAqEDoQORBJEEagVqBSIGIga9Br0GRAdEB7UHtQcSCBIIWwhbCIcIhwifCJ8IrwivCLsIuwjUCNQIBQkFCUsJSwmtCa0JMAowCskKyQp1C3ULLAwsDNQM1AxeDV4NxA3EDfUN9Q30DfQN0A3QDY0NjQ07DTsN8AzwDKwMrAx3DHcMWQxZDEQMRAw5DDkMOQw5DDcMNwwxDDEMIwwjDP0L/Qu1C7ULSAtIC60KrQrjCeMJ9Aj0CN8H3weoBqgGVwVXBe8D7wN4AngCAAEAAZr/mv9U/lT+PP08/Vv8W/yt+637KPso+8X6xfp5+nn6Ovo6+gn6CfrZ+dn5nfmd+VT5VPn7+Pv4lviW+D/4P/gH+Af4+vf69x34Hfhm+Gb4wfjB+CT5JPmK+Yr57/nv+Vv6W/rL+sv6Ofs5+577nvvz+/P7L/wv/FP8U/xY/Fj8Nvw2/Ov76/tv+2/7w/rD+vj5+PkX+Rf5Kfgp+Dz3PPdE9kT2MfUx9Qf0B/Sz8rPyL/Ev8Y3vje/A7cDtvOu865bplulC50Lnv+S/5EDiQOLX39ffl92X3bzbvNtR2lHaVNlU2ebY5tj02PTYWtla2RfaF9oD2wPb7dvt293c3dzD3cPdl96X3nnfed9t4G3gZuFm4Xfid+KZ45njwOTA5ALmAuZk52Tn3ejd6HjqeOov7C/s9u327dXv1e/R8dHx5fPl8xH2EfZM+Ez4g/qD+qn8qfy5/rn+rwCvAIMCgwI5BDkEzwXPBT8HPwedCJ0IAAoACnQLdAsUDRQN7A7sDuUQ5RD1EvUSDxUPFQoXChfbGNsYfxp/Gs4bzhvKHMocfx1/Hd4d3h0FHgUeGB4YHhMeEx4JHgkeCx4LHv4d/h3kHeQdxh3GHYcdhx0qHSoduxy7HCgcKBx+G34b1xrXGiYaJhpxGXEZvBi8GOkX6RfwFvAW1hXWFZAUkBQpEykTtRG1ETQQNBCyDrIOQg1CDesL6wu8CrwKwgnCCfcI9whQCFAIvQe9ByUHJQd2BnYGqgWqBbsEuwSsA6wDhgKGAlIBUgEfAB8A/f79/v/9//0z/TP9m/yb/Dj8OPz6+/r70PvQ+7L7svub+5v7ivuK+437jfus+6z76fvp+0T8RPyz/LP8Lf0t/ar9qv0m/ib+nf6d/gv/C/9u/27/wv/C/wgACABHAEcAjACMAOEA4QBQAVAB1gHWAWkCaQL6AvoCfQN9A+QD5AMoBCgERgRGBDkEOQQDBAMErQOtA0IDQgPWAtYCeQJ5AjUCNQILAgsC9AH0AegB6AHeAd4B0gHSAb8BvwGfAZ8BbQFtASUBJQHIAMgAWwBbAOr/6v95/3n/Cv8K/5z+nP4m/ib+pv2m/SP9I/2h/KH8J/wn/L/7v/tp+2n7Jfsl+/n6+frq+ur69fr1+hv7G/tP+0/7fvt++6H7ofuw+7D7rPus+6D7oPuS+5L7gvuC+3b7dvtx+3H7ePt4+5b7lvvR+9H7Jvwm/JH8kfwD/QP9b/1v/dD90P0f/h/+VP5U/mr+av5Y/lj+G/4b/rz9vP1K/Ur91vzW/HX8dfwu/C78/fv9++L74vvR+9H7wvvC+7X7tfui+6L7hvuG+2T7ZPtD+0P7LPss+y77LvtR+1H7lPuU+/L78vti/GL81fzV/EP9Q/2p/an9A/4D/lf+V/6t/q3+D/8P/4P/g/8NAA0ApgCmAD8BPwHOAc4BTQJNArkCuQIUAxQDXwNfA5MDkwOqA6oDqAOoA5ADkANxA3EDXQNdA1oDWgNrA2sDjgOOA7wDvAP0A/QDNwQ3BHwEfAS7BLsE6gTqBP4E/gT5BPkE6AToBNQE1ATHBMcEwgTCBLwEvAStBK0ElQSVBHIEcgRJBEkEIAQgBPMD8wPGA8YDogOiA48DjwOeA54D1wPXAzMEMwSqBKoEMQUxBbkFuQVFBkUG1wbXBmoHagf8B/wHiAiICAEJAQlpCWkJygnKCSIKIgp4CngKzArMCg0LDQs0CzQLPgs+CyMLIwvlCuUKjAqMCg8KDwpvCW8JtQi1COMH4wcIBwgHNQY1BnEFcQW+BL4EGQQZBHUDdQPJAskCEAIQAkoBSgF6AHoAp/+n/9f+1/4P/g/+Vf1V/bP8s/wy/DL81/vX+6r7qvul+6X7vvu+++r76vsb/Bv8SvxK/Hj8ePyu/K788/zz/E79Tv27/bv9Lf4t/pX+lf7i/uL+DP8M/xT/FP/8/vz+xv7G/nD+cP7w/fD9Qf1B/XH8cfyQ+5D7tPq0+u357fkk+ST5Mfgx+Pv2+/Zi9WL1ZfNl8zPxM/He7t7ubuxu7Pvp++l253bn3eTd5GriauI64DrgXt5e3vzc/Nz+2/7bQ9tD2+Xa5drj2uPaNds12+/b79vk3OTc0t3S3a7ert5k32Tf9d/135TglOBJ4Unh/eH94bbituJl42XjB+QH5Mfkx+S95b3l5ubm5kroSujX6dfpdut26y3tLe397v3u2vDa8MLywvKp9Kn0gfaB9k74TvgY+hj63vve+539nf1R/1H/+AD4AJMCkwI2BDYE8AXwBb4HvgepCakJtAu0C8sNyw30D/QPJRIlEjMUMxQLFgsWphemF+sY6xjuGe4ZxhrGGmEbYRvBG8Eb8RvxG+Qb5Bu+G74bqhuqG6EboRumG6YbsBuwG5QblBtUG1QbBRsFG6EaoRoxGjEauBm4GRcZFxlNGE0YbBdsF3QWdBZzFXMVchRyFFsTWxMsEiwS9RD1EL4Pvg+cDpwOoA2gDb8MvwzzC/MLPQs9C5cKlwoFCgUKhwmHCQkJCQl5CHkIzAfMB/oG+gYEBgQG8wTzBM0DzQOXApcCYAFgAT4APgBE/0T/gv6C/vj9+P2Y/Zj9T/1P/Rf9F/3r/Ov8zfzN/L38vfyy/LL8oPyg/Ib8hvxp/Gn8VPxU/FP8U/xp/Gn8jvyO/L78vvz5/Pn8P/0//Zn9mf0I/gj+i/6L/h3/Hf+//7//eAB4AEQBRAEgAiAC/AL8Ar8DvwNZBFkExgTGBAQFBAUcBRwFFAUUBe4E7gS0BLQEdgR2BEIEQgQqBCoEMgQyBFAEUAR3BHcEnQSdBLkEuQTMBMwE1wTXBM8EzwSpBKkEYQRhBPYD9gN1A3UD7QLtAmkCaQLsAewBcAFwAfQA9AB2AHYA/P/8/47/jv8t/y3/1v7W/oj+iP4//j/+/f39/cb9xv2Y/Zj9bf1t/T39Pf3//P/8qvyq/EX8RfzV+9X7ZPtk+wT7BPvA+sD6ofqh+q/6r/rl+uX6Nfs1+5P7k/vu++77Nfw1/GL8Yvxn/Gf8Pfw9/OX75ftm+2b7y/rL+jD6MPql+aX5M/kz+d/43/ii+KL4c/hz+Fj4WPhU+FT4avhq+KL4ovj2+Pb4XPlc+dH50flT+lP64frh+nz7fPse/B78u/y7/Ef9R/29/b39HP4c/mz+bP64/rj+Bf8F/1b/Vv+t/63/CAAIAGkAaQDTANMATAFMAdEB0QFhAmEC9QL1An4DfgP2A/YDWgRaBKcEpwTkBOQEHAUcBVIFUgWMBYwFzwXPBRUGFQZfBl8GqgaqBu0G7QYfBx8HOgc6BzIHMgcHBwcHvQa9BlkGWQbkBeQFaQVpBekE6QRmBGYE5APkA2IDYgPiAuICaAJoAvcB9wGTAZMBQQFBAQYBBgHmAOYA4gDiAPUA9QAcARwBUAFQAY0BjQHTAdMBJAIkAnwCfALZAtkCNwM3A4sDiwPTA9MDEwQTBEkESQR7BHsErgSuBNoE2gT9BP0EFwUXBSAFIAUZBRkFAgUCBdUE1QSNBI0EKQQpBKkDqQMWAxYDggKCAvsB+wGNAY0BOQE5AfgA+AC/AL8AhACEAEIAQgD7//v/sv+y/27/bv8v/y//+P74/sz+zP6y/rL+rf6t/r7+vv7f/t/+Bv8G/yr/Kv9K/0r/bP9s/5n/mf/U/9T/GgAaAF8AXwCUAJQAtgC2AMUAxQDKAMoAygDKAMUAxQCxALEAhACEADwAPADZ/9n/Y/9j/+L+4v5O/k7+n/2f/dH80fzX+9f7sPqw+mf5Z/n19/X3VPZU9o30jfST8pPyYPBg8BHuEe6n66frJ+kn6brmuuZo5GjkP+I/4nPgc+AU3xTfI94j3r7dvt3O3c7dLN4s3tTe1N6i36Lfc+Bz4FHhUeEr4ivi7uLu4qvjq+Nc5Fzk9OT05I/lj+Ux5jHm2+bb5qvnq+ek6KTouem56fPq8+pD7EPslO2U7eju6O428Dbwd/F38bfyt/L78/vzRvVG9aT2pPYY+Bj4nfmd+Sv7K/u+/L78Tf5N/tP/0/9gAWABAQMBA70EvQSpBqkGywjLCBALEAtxDXEN4A/gDzMSMxJdFF0UWBZYFgEYARhcGVwZdRp1GjwbPBvHG8cbMhwyHHUcdRyqHKoc6RzpHB4dHh1PHU8dfx1/HYodih1sHWwdKR0pHakcqRz0G/QbHhseGx4aHhoBGQEZ1xfXF5YWlhZEFUQV7xPvE44SjhIqESoRzw/PD3gOeA4oDSgN6wvrC74KvgqqCaoJugi6CO0H7Qc/Bz8HqQapBhgGGAZ/BX8F1ATUBBEEEQQ1AzUDRgJGAkgBSAFBAEEAPv8+/1D+UP6D/YP94vzi/HL8cvwo/Cj89fv1+877zvum+6b7dft1+z/7P/sD+wP7wPrA+nz6fPo9+j36CfoJ+vL58vkB+gH6Nvo2+pH6kfoH+wf7i/uL+xT8FPyf/J/8Kv0q/bX9tf1C/kL+1P7U/mv/a/8OAA4AuwC7AG4BbgEcAhwCugK6AjoDOgOWA5YD1QPVA/0D/QMZBBkEOAQ4BFsEWwSMBIwE0ATQBCYFJgWOBY4FAgYCBnEGcQbNBs0GDAcMByUHJQccBxwH/gb+BtIG0gaiBqIGegZ6BlcGVwY+Bj4GLwYvBiAGIAYIBggG3QXdBZQFlAUsBSwFrQStBCAEIASRA5EDCQMJA4kCiQIRAhECoQGhATUBNQHIAMgAUQBRAMX/xf8V/xX/O/47/j79Pv0t/C38Hfsd+y36Lfpr+Wv52/jb+Hz4fPg4+Dj49vf296z3rPdL90v30fbR9lL2Uvbd9d31gfWB9Vb1VvVf9V/1mPWY9QL2AvaT9pP2PPc89/z3/PfM+Mz4o/mj+YH6gfph+2H7PPw8/An9Cf3F/cX9bP5s/v/+//6J/4n/EwATAJ8AnwAsASwBsAGwARoCGgJfAl8CfAJ8AnQCdAJSAlICKQIpAgUCBQL3AfcBBgIGAjQCNAJ8AnwC0QLRAiMDIwNlA2UDkwOTA6oDqgOzA7MDugO6A8QDxAPaA9oDAgQCBDkEOQSABIAE1QTVBCsFKwV6BXoFvwW/Be8F7wUPBg8GJAYkBi0GLQYsBiwGKAYoBh4GHgYRBhEGCQYJBgAGAAbzBfMF4AXgBbsFuwWFBYUFQwVDBfkE+QSvBK8EbgRuBDUENQQIBAgE6wPrA9wD3APfA98D8wPzAw4EDgQpBCkENgQ2BCcEJwTxA/EDkQORAwQDBANPAk8CfwF/AaMAowDI/8j/9/73/jj+OP6F/YX91vzW/Cb8Jvxv+2/7rvqu+vP58/lG+Ub5sfix+ET4RPj+9/732ffZ99L30vfg9+D39/f39xf4F/g9+D34Y/hj+JD4kPjI+Mj4EvkS+Xr5evkH+gf6tvq2+oD7gPtY/Fj8L/0v/f79/v2+/r7+av9q//z//P9rAGsAsgCyAM4AzgDJAMkAsACwAJEAkQB0AHQAWABYADkAOQARABEA2//b/5j/mP9C/0L/zv7O/jT+NP5g/WD9R/xH/PX69fpq+Wr5r/ev99f11/Xe897zv/G/8ZDvkO9Q7VDtCusK6+zo7OgG5wbnZuVm5TbkNuR543njIOMg4zXjNeOV45XjE+QT5Kfkp+Qx5THlm+Wb5frl+uVN5k3mlOaU5uvm6+ZW51bn0OfQ53TodOhC6ULpNOo06lbrVuud7J3s9e317VvvW+/D8MPwHPIc8mjzaPOj9KP0yvXK9eT25Pb29/b3APkA+Qb6BvoL+wv7DfwN/A39Df0T/hP+Jv8m/0wATACVAZUBCwMLA6sEqwR7BnsGdwh3CIQKhAqbDJsMsQ6xDqYQphB1EnUSHBQcFIMVgxWzFrMWwBfAF6YYphiDGYMZdxp3GnEbcRt3HHcchx2HHXUedR47Hzsf2x/bHzggOCBUIFQgPCA8INQf1B8nHycfTx5PHkYdRh0iHCIc+xr7GsAZwBl1GHUYJxcnF84VzhV6FHoUPxM/ExUSFRL7EPsQ8A/wD+cO5w7jDeMN8AzwDAsMCwwuCy4LUQpRCl0JXQlFCEUIDAcMB7cFtwVWBFYE+gL6Aq8BrwF9AH0Aa/9r/4T+hP7H/cf9Lv0u/bL8svw4/Dj8qfup+/36/for+iv6Nvk2+Tb4Nvg09zT3OfY59ln1WfWa9Jr0/vP+85LzkvNW81bzPfM980vzS/N283bzs/Oz8wv0C/SA9ID0DvUO9bv1u/WB9oH2U/dT9yz4LPgG+Qb51fnV+Zj6mPpI+0j74vvi+2j8aPzm/Ob8Z/1n/fv9+/2r/qv+dv92/1AAUAAwATABCgIKAtUC1QKRA5EDOwQ7BMwEzARBBUEFmQWZBdMF0wX3BfcFFQYVBjAGMAZSBlIGgAaABrQGtAbvBu8GNgc2B4EHgQfRB9EHJggmCHEIcQitCK0I1AjUCNsI2wjBCMEIiwiLCDkIOQjRB9EHXAdcB9kG2QZJBkkGrgWuBQgFCAVXBFcEpQOlA/QC9AJJAkkCpwGnARABEAGCAIIA/v/+/4T/hP8L/wv/if6J/vj9+P1U/VT9n/yf/O/77/tR+1H70frR+nj6ePpA+kD6Ifoh+hb6Fvoa+hr6J/on+kH6Qfph+mH6gfqB+qf6p/rR+tH6//r/+jP7M/to+2j7lvuW+7/7v/vp++n7HPwc/GX8ZfzI/Mj8QP1A/cH9wf1D/kP+v/6//jf/N/+t/63/JAAkAJQAlAD4APgATQFNAZIBkgHMAcwB/wH/ASsCKwJPAk8CZwJnAnMCcwJ4AngCfwJ/Ao4CjgKpAqkC0gLSAgADAAMyAzIDZANkA5ADkAO0A7QDzAPMA9MD0wPLA8sDvAO8A6oDqgOjA6MDrwOvA88DzwMEBAQESgRKBJgEmATsBOwERgVGBZ8FnwX4BfgFUAZQBp8GnwbfBt8GEQcRBy0HLQc4BzgHNgc2ByEHIQf3BvcGuAa4Bl0GXQboBegFYAVgBckEyQQpBCkEggOCA9QC1AIcAhwCWgFaAZEAkQDD/8P/8f7x/h/+H/5H/Uf9Z/xn/IX7hfue+p76t/m3+dv42/gP+A/4VvdW97z2vPZG9kb28PXw9cL1wvW29bb1vvW+9dn12fX79fv1GfYZ9jr2OvZd9l32hfaF9r32vfYH9wf3XPdc9733vfck+CT4iPiI+O747vhU+VT5tvm2+RX6Ffpo+mj6qPqo+tX61frr+uv66Pro+s76zvqU+pT6Lvou+p35nfnW+Nb42PfY97H2sfZc9Vz11/PX8zTyNPJw8HDwkO6Q7rjsuOz16vXqUOlQ6fLn8uff5t/mFeYV5qflp+WB5YHlguWC5arlquXf5d/lDuYO5kzmTOab5pvm8+bz5mnnaef15/XniuiK6DvpO+kK6grq8ury6gDsAOwt7S3tY+5j7qbvpu/v8O/wNfI18n/zf/PN9M30FfYV9lv3W/eg+KD44/nj+Sf7J/tn/Gf8nP2c/b3+vf7O/87/1ADUANYB1gHkAuQCBwQHBDoFOgWHBocG8gfyB28JbwkACwALngyeDCoOKg6bD5sP7RDtEBASEBITExMTCRQJFOgU6BTDFcMVrBasFpcXlxeTGJMYrBmsGb8avxrIG8gbwhzCHIsdix0oHigeqh6qHvke+R4eHx4fJx8nH/8e/x6zHrMeXB5cHucd5x1YHVgdthy2HOQb5BvoGuga1hnWGawYrBh5F3kXTxZPFh4VHhXoE+gTtxK3EoYRhhFcEFwQQw9DDywOLA4RDREN8QvxC8YKxgqYCZgJdAh0CF4HXgdbBlsGcwVzBaYEpgT3A/cDZgNmA+wC7AJ7AnsCAgICAncBdwHRANEADwAPADv/O/9Z/ln+cf1x/Y38jfyy+7L74/rj+ij6KPp8+Xz51/jX+Dr4Ovie9573//b/9mf2Z/bX9df1S/VL9c70zvRb9Fv07/Pv85jzmPNU81TzHPMc8/vy+/Ln8ufy2fLZ8tny2fLp8unyCvMK803zTfO287bzQPRA9O307fSz9bP1hfaF9l73Xvc8+Dz4F/kX+fP58/nK+sr6kvuS+0b8Rvzk/OT8bv1u/fD98P16/nr+E/8T/77/vv91AHUALwEvAd8B3wF+An4CAgMCA2ADYAOVA5UDoAOgA4UDhQNWA1YDJwMnAwQDBAP2AvYC/gL+AhIDEgMqAyoDRANEA1oDWgNwA3ADiQOJA6YDpgPLA8sDAAQABEIEQgSQBJAE5wTnBDkFOQWBBYEFvwW/BfEF8QUfBh8GTwZPBnkGeQafBp8GvQa9BtAG0AbcBtwG6AboBvAG8AbyBvIG6QbpBsoGygaQBpAGPAY8BssFywVBBUEFowSjBPED8QMxAzEDawJrAqoBqgH1APUAVABUAMr/yv9S/1L/5v7m/oH+gf4f/h/+u/27/Vr9Wv39/P38pfyl/Ff8V/wU/BT83vve+7r7uvup+6n7p/un+7H7sfvD+8P71PvU++f75/v/+//7IPwg/FP8U/yX/Jf86vzq/Ej9SP2t/a39Fv4W/n3+ff7i/uL+P/8//5P/k//f/9//KAAoAHIAcgDAAMAAEgESAWMBYwGzAbMBAAIAAkwCTAKcApwC7wLvAkADQAOKA4oDywPLAwAEAAQtBC0EVgRWBHoEegSXBJcEqgSqBLAEsASrBKsEogSiBJQElASDBIMEbgRuBE8ETwQlBCUE8wPzA7kDuQN4A3gDMwMzA+oC6gKdAp0CUQJRAgYCBgK8AbwBbgFuARgBGAGyALIAOwA7ALf/t/8p/yn/lP6U/v79/v1p/Wn91vzW/E78TvzU+9T7aPto+xH7EfvH+sf6hfqF+kz6TPoW+hb63/nf+a35rfl8+Xz5SPlI+RX5Ffnj+OP4rfit+Hv4e/hH+Ef4C/gL+ND30PeT95P3U/dT9xr3Gvfj9uP2pvam9mL2YvYN9g32m/Wb9Rf1F/V49Hj0tfO189by1vLK8crxhfCF8BnvGe+E7YTtzuvO6x7qHuqC6ILoBOcE58nlyeXW5NbkIeQh5MDjwOOh46HjpuOm49Xj1eMV5BXkT+RP5JTklOTf5N/kKeUp5Yvli+UF5gXmjeaN5jjnOOcE6ATo6ejp6Pnp+eky6zLriuyK7APuA+6U75TvKfEp8cDywPJV9FX05PXk9XL3cvcA+QD5h/qH+v37/fta/Vr9mf6Z/rr/uv/IAMgA0QHRAdoC2gLsA+wDDQUNBTYGNgZxB3EHxAjECCIKIgqLC4sL9wz3DEsOSw6BD4EPnRCdEJARkBFpEmkSOBM4E/UT9ROxFLEUghWCFV4WXhZXF1cXeBh4GJ0ZnRnAGsAa1xvXG7wcvBxvHW8dAB4AHlseWx6MHowepx6nHpgemB5qHmoeMx4zHt8d3x15HXkdDh0OHYcchxzsG+wbSxtLG5YalhraGdoZJxknGW0YbRivF68X9Rb1FiwWLBZVFVUVchRyFHETcRNMEkwSCBEIEZ4Png8aDhoOkgySDBELEQujCaMJWQhZCDEHMQcsBiwGSwVLBYQEhATOA84DHQMdA2MCYwKWAZYBtgC2AMr/yv/f/t/+Av4C/kL9Qv2j/KP8I/wj/MH7wft3+3f7Pvs++xn7GfsB+wH77/rv+uP64/rV+tX6wfrB+q36rfqZ+pn6hPqE+nD6cPpT+lP6Ifoh+tr52vl4+Xj5/fj9+Hr4evj39/f3dfd19wT3BPej9qP2U/ZT9iH2IfYP9g/2FvYW9jb2NvZd9l32ePZ49oX2hfZ89nz2XfZd9jb2NvYQ9hD28/Xz9fL18vUc9hz2dvZ29gj3CPfK98r3ovii+H75fvlH+kf68vry+oL7gvsB/AH8dfx1/Or86vxi/WL93f3d/Vz+XP7f/t/+Y/9j/+H/4f9VAFUAuwC7ABUBFQFsAWwByQHJAS0CLQKbApsCDQMNA3wDfAPmA+YDSwRLBKgEqAT8BPwEQwVDBXEFcQWEBYQFgAWABWYFZgVCBUIFIAUgBQQFBAXzBPME9AT0BAMFAwUhBSEFTAVMBXYFdgWXBZcFqwWrBacFpwWPBY8FaAVoBTUFNQUABQAF0QTRBKcEpwSGBIYEbQRtBFUEVQQ3BDcEEQQRBN4D3gOeA54DVANUAwMDAwOuAq4CWQJZAggCCAK+Ab4BfQF9AUMBQwEIAQgBxADEAHIAcgAMAAwAlv+W/xv/G/+m/qb+QP5A/vP98/29/b39nP2c/Yz9jP2K/Yr9kf2R/Z79nv2u/a79u/27/cb9xv3N/c391P3U/eH94f35/fn9Hv4e/k/+T/6L/ov+0P7Q/h3/Hf9x/3H/yf/J/yIAIgB5AHkAyQDJABMBEwFXAVcBmgGaAdkB2QEUAhQCRgJGAmgCaAJ5AnkCeQJ5AmcCZwJDAkMCCwILAr0BvQFZAVkB4wDjAGUAZQDn/+f/cP9w/wT/BP+e/p7+Ov46/tT91P1n/Wf98/zz/H38ffwG/Ab8j/uP+yD7IPu7+rv6X/pf+hX6Ffrc+dz5tPm0+aH5ofmg+aD5qfmp+b/5v/ne+d75AvoC+jT6NPp0+nT6wPrA+hf7F/tw+3D7wPvA+wP8A/w2/Db8VvxW/Gf8Z/xn/Gf8VfxV/DX8NfwK/Ar82PvY+6X7pftw+3D7Mfsx++P64/p5+nn65Pnk+Sb5Jvkz+DP4A/cD96X1pfUX9Bf0XvJe8pzwnPDZ7tnuIO0g7ZPrk+s26jbqBekF6RjoGOhh52HnyubK5lrmWub95f3ln+Wf5VblVuUe5R7l7+Tv5OHk4eTs5OzkBOUE5T3lPeWW5ZblCuYK5rLmsuaK54rnhOiE6KjpqOnt6u3qROxE7LXtte097z3v0PDQ8HDycPIT9BP0qfWp9S73Lvee+J749/n3+T37Pft8/Hz8uP24/fL+8v4zADMAfAF8Ac4CzgIzBDMEsQWxBToHOgfHCMcISwpLCqkLqQvdDN0M7Q3tDdUO1Q6nD6cPdRB1EDURNRHyEfIRuxK7EoMTgxNaFFoUTBVMFUAWQBY3FzcXLhguGAUZBRnAGcAZaBpoGuoa6hpSG1IbsRuxG/Mb8xsjHCMcThxOHFscWxxQHFAcOhw6HAUcBRzAG8Abfxt/GzQbNBvmGuYanxqfGkYaRhrfGd8Zdxl3Gf0Y/Rh3GHcY6xfrFz8XPxdvFm8WgxWDFXIUchRLE0sTJxInEgURBRHsD+wP5Q7lDuUN5Q3tDO0MAgwCDBsLGws0CjQKSwlLCVIIUghDB0MHHgYeBucE5wSlA6UDZAJkAi8BLwELAAsA/P78/gX+Bf4l/SX9W/xb/Kv7q/sT+xP7j/qP+iX6JfrR+dH5j/mP+WP5Y/lL+Uv5QPlA+Ub5RvlT+VP5Xvle+Wj5aPlq+Wr5X/lf+U75Tvk5+Tn5Jvkm+SD5IPkr+Sv5SPlI+Xn5efm3+bf59/n3+Tj6OPpv+m/6kfqR+p76nvqN+o36XPpc+hL6Evq4+bj5VflV+fn4+fip+Kn4ZPhk+C74LvgC+AL42ffZ97b3tveT95P3afdp90L3Qvcf9x/3B/cH9wf3B/ck9yT3V/dX95/3n/fy9/L3R/hH+KT4pPgM+Qz5f/l/+QL6AvqS+pL6Kvsq+837zft9/H38Pf09/Qz+DP7n/uf+wv/C/5MAkwBSAVIB/wH/AZgCmAIeAx4DlQOVA/oD+gNQBFAEoASgBOsE6wQ4BTgFiwWLBdwF3AUpBikGbQZtBqEGoQbFBsUG2gbaBtwG3AbLBssGqwarBnkGeQY9Bj0G/QX9BbkFuQVzBXMFLAUsBd0E3QSJBIkEMgQyBNsD2wOIA4gDPgM+A/wC/ALGAsYCngKeAoICggJzAnMCbQJtAmgCaAJbAlsCPwI/AhICEgLYAdgBmQGZAWABYAE0ATQBGgEaARABEAEXARcBLAEsAUwBTAFzAXMBnQGdAb8BvwHUAdQB2AHYAc0BzQG3AbcBngGeAYMBgwFpAWkBUgFSAT4BPgEtAS0BJAEkASMBIwEnAScBMAEwATkBOQFAAUABRgFGAUkBSQFHAUcBPgE+ASsBKwEKAQoB3QDdAKQApABhAGEAFQAVAML/wv9n/2f/Bv8G/6X+pf5H/kf+8P3w/aX9pf1o/Wj9OP04/Rb9Fv39/P385/zn/M/8z/yv/K/8gfyB/En8SfwL/Av8yvvK+5L7kvto+2j7S/tL+z37Pfs6+zr7Ofs5+zv7O/s6+zr7Mvsy+yn7Kfsc+xz7DPsM+/36/frt+u361/rX+r76vvqe+p76cfpx+j36Pfr8+fz5rvmu+Vv5W/kE+QT5q/ir+Ff4V/gH+Af4uve693b3dvc69zr3AfcB9872zvaW9pb2SfZJ9uj16PVl9WX1t/S39Ozz7PP/8v/y8fHx8dbw1vCy77Lvje6N7obthu2o7Kjs8uvy63nreesv6y/r/+r/6u3q7erm6ubq2OrY6tHq0erI6sjqsuqy6p/qn+qK6orqa+pr6lrqWupX6lfqYupi6o/qj+re6t7qRetF687rzutz7HPsKu0q7f/t/+3x7vHu9u/27xDxEPE08jTyTvNO8130XfRc9Vz1SvZK9jP3M/cf+B/4EfkR+Qz6DPoW+xb7Mfwx/GL9Yv2x/rH+IAAgAKYBpgE8AzwD1QTVBF0GXQbQB9AHLgkuCWwKbAqVC5ULsgyyDLsNuw28DrwOvg++D7gQuBCzEbMRuxK7ErwTvBO8FLwUvxW/FagWqBZ3F3cXLRgtGLIYshgNGQ0ZUhlSGXQZdBmDGYMZjhmOGYIZghlkGWQZPxk/GQEZARm6GLoYfxh/GEIYQhgMGAwY5xfnF7sXuxeMF4wXZhdmFzkXORcNFw0X6RbpFrQWtBZqFmoWExYTFp8VnxUaFRoVmxSbFB0UHRSrE6sTUBNQE/oS+hKqEqoSYhJiEg4SDhKrEasRPBE8EbAQsBAEEAQQPg8+D1YOVg5ODU4NMwwzDAcLBwvXCdcJsgiyCJoHmgeNBo0GjAWMBYwEjASLA4sDiwKLApEBkQGeAJ4Asv+y/83+zf7o/ej9A/0D/Sf8J/xZ+1n7m/qb+vP58/lX+Vf5vvi++Cv4K/ic95z3FPcU96T2pPZM9kz2DPYM9uj16PXe9d714/Xj9f31/fUh9iH2RvZG9mz2bPaO9o72qvaq9sf2x/bp9un2DPcM9zj3OPds92z3pPek9+b35vcs+Cz4bPhs+KT4pPjH+Mf4zvjO+ML4wviq+Kr4jviO+H/4f/iB+IH4k/iT+Lb4tvjj+OP4E/kT+Uf5R/l++X75tPm0+fL58vk1+jX6e/p7+sX6xfoR+xH7Xvte+7L7svsQ/BD8e/x7/PL88vxt/W394/3j/U7+Tv6u/q7+Cv8K/2n/af/Q/9D/PAA8AKYApgAGAQYBWgFaAaIBogHoAegBMAIwAngCeAK8ArwC9wL3AiIDIgNBA0EDXQNdA3kDeQObA5sDxgPGA/ID8gMaBBoEPQQ9BFEEUQRWBFYETQRNBDQENAQRBBEE6gPqA8ADwAOWA5YDcQNxA1ADUAM4AzgDLgMuAy8DLwM3AzcDOgM6Ay0DLQMKAwoD1wLXApwCnAJjAmMCMwIzAgcCBwLYAdgBowGjAWcBZwEsASwB+QD5ANMA0wC4ALgApACkAJMAkwCJAIkAigCKAJsAmwC+AL4A8ADwACcBJwFdAV0BjwGPAb4BvgHtAe0BIAIgAlYCVgKQApAC0QLRAhUDFQNdA10DqAOoA+4D7gMrBCsEWgRaBHYEdgR+BH4EdAR0BFMEUwQaBBoEygPKA2MDYwPrAusCZwJnAuAB4AFZAVkB1QDVAFUAVQDW/9b/WP9Y/93+3f5i/mL+6f3p/XX9df0E/QT9mPyY/DP8M/zW+9b7fvt++zH7Mfvu+u76s/qz+ob6hvpl+mX6TPpM+j/6P/o8+jz6P/o/+k76Tvpn+mf6h/qH+q/6r/rX+tf69fr1+gX7BfsB+wH75/rn+r76vvqN+o36VfpV+iH6Ifrw+fD5v/m/+ZT5lPlt+W35RPlE+Rr5Gvng+OD4hPiE+Ab4BvhY91j3ePZ49nT1dPVO9E70BvMG87DxsPFK8Erw1e7V7nLtcu0q7CrsA+sD6xzqHOps6Wzp4+jj6Ifoh+hD6EPoA+gD6Nnn2ee657rnmeeZ54bnhudy53LnS+dL5yXnJecE5wTn6+br5v7m/uZC50LnrOes50joSOgE6QTp0OnQ6bfqt+q167XrwOzA7N7t3u0A7wDvEfAR8BHxEfH98f3x1fLV8qzzrPOM9Iz0d/V39XX2dfaJ94n3sPiw+PD58PlM+0z7w/zD/E7+Tv7s/+z/lQGVATkDOQPXBNcEaQZpBuQH5AdMCUwJqgqqCvQL9As5DTkNgQ6BDsAPwA/+EP4QRxJHEo0TjRPbFNsUOBY4FoUXhRe5GLkYzxnPGaYaphpDG0MbuBu4G/kb+RsTHBMcFxwXHPEb8RupG6kbUhtSG+Ma4xpsGmwaAxoDGpYZlhkqGSoZxxjHGFYYVhjcF9wXZRdlF+EW4RZVFlUWyRXJFS0VLRWBFIEU0RPRExATEBNMEkwSlRGVEegQ6BBOEE4Qzw/PD18PXw/9Dv0Org6uDmUOZQ4kDiQO7g3uDbINsg1qDWoNFg0WDasMqwwrDCsMoguiCwsLCwtxCnEK3gneCVAJUAnNCM0IWwhbCPIH8geRB5EHNgc2B9kG2QZ6BnoGGgYaBrUFtQVHBUcFzQTNBEMEQwSnA6cD/wL/Ak8CTwKdAZ0B7ADsAD0APQCQ/5D/5f7l/kP+Q/6p/an9G/0b/Zv8m/wl/CX8tfu1+0z7TPvl+uX6f/p/+hv6G/q4+bj5UvlS+fH48fiX+Jf4QvhC+Pn3+fe597n3evd690P3Q/cR9xH34/bj9sD2wPaj9qP2gvaC9mH2YfY39jf2AvYC9tH10fWo9aj1ifWJ9X31ffWB9YH1jvWO9a31rfXe9d71IfYh9n72fvby9vL2cfdx9/n3+feE+IT4C/kL+Zf5l/kr+iv6xvrG+mv7a/sT/BP8ufy5/Fj9WP3w/fD9gf6B/g7/Dv+a/5r/JQAlAKoAqgApASkBnQGdAQECAQJVAlUCmwKbAtMC0wICAwIDKwMrA00DTQNoA2gDfQN9A4cDhwOJA4kDhgOGA34DfgNyA3IDYQNhA0YDRgMfAx8D8QLxArwCvAKGAoYCUQJRAhsCGwLfAd8BnQGdAVUBVQEOAQ4BzQDNAJUAlQBlAGUAOgA6ABAAEADl/+X/u/+7/5X/lf91/3X/Wv9a/0T/RP8x/zH/H/8f/xL/Ev8M/wz/Dv8O/xn/Gf8r/yv/RP9E/2T/ZP+K/4r/s/+z/97/3v8IAAgAMgAyAFkAWQCCAIIAqgCqANEA0QD1APUAFwEXATcBNwFbAVsBiAGIAb4BvgH9Af0BPwI/AnsCewKvAq8C2QLZAvcC9wIKAwoDEwMTAw0DDQP4AvgC1wLXAqwCrAJ5AnkCQwJDAgoCCgLPAc8BkQGRAVEBUQEQARAB0ADQAJMAkwBYAFgAHwAfAOz/7P+8/7z/j/+P/2X/Zf83/zf/Av8C/8f+x/6H/of+Rv5G/gv+C/7Z/dn9r/2v/Y79jv11/XX9Yf1h/VX9Vf1O/U79R/1H/T79Pv0t/S39D/0P/ev86/zB/MH8k/yT/Gn8afxA/ED8F/wX/PH78fvP+8/7sPuw+5j7mPuF+4X7bftt+0/7T/sh+yH73Prc+oL6gvoM+gz6dfl1+cP4w/jq9+r35fbl9sD1wPWA9ID0LPMs8+bx5vG38Lfwpe+l78TuxO4K7gruau1q7e/s7+yN7I3sNew17PHr8euz67PraOto6xjrGOu56rnqQOpA6sfpx+lR6VHp2+jb6IXohehP6E/oMugy6EHoQehy6HLotei16BfpF+mP6Y/pEOoQ6qfqp+pH60fr3+vf63XsdewB7QHtfe197QHuAe6U7pTuNO807/Dv8O/F8MXwqvGq8azyrPLR89HzGfUZ9Yn2ifYa+Br4vfm9+WT7ZPsH/Qf9of6h/i4ALgC0AbQBNQM1A6sEqwQfBh8GmQeZBxMJEwmZCpkKNgw2DNoN2g2LD4sPTBFMEQATABOjFKMUMBYwFoYXhhenGKcYnRmdGVsaWxrwGvAacBtwG8cbxxsAHAAcKRwpHDIcMhwsHCwcLhwuHCccJxwgHCAcIRwhHA4cDhzrG+sbwhvCG30bfRslGyUbxRrFGkUaRRqsGawZAxkDGT0YPRhmF2YXkBaQFrMVsxXZFNkUDhQOFEYTRhOCEoISyxHLERcRFxFoEGgQxA/EDxsPGw9oDmgOqg2qDdQM1AzpC+kL8wrzCvYJ9gn6CPoICwgLCCkHKQdVBlUGlQWVBeYE5gRLBEsExgPGA1ADUAPnAucChQKFAiYCJgLGAcYBZQFlAQUBBQGlAKUASQBJAPL/8v+i/6L/Wf9Z/xn/Gf/j/uP+tv62/pP+k/57/nv+bf5t/mn+af5u/m7+df51/n7+fv6B/oH+e/57/mr+av5O/k7+KP4o/vz9/P3N/c39mv2a/Wf9Z/0y/TL9+Pz4/Lr8uvx2/Hb8J/wn/NL70vt0+3T7C/sL+5z6nPol+iX6pvmm+Sn5Kfmv+K/4OPg4+M73zvdu9273FvcW9832zfaW9pb2cfZx9m32bfaI9oj2uva69gD3APdK90r3jveO99D30PcW+Bb4ZPhk+MT4xPgw+TD5nvme+Qj6CPpr+mv6yvrK+i77Lvue+577FvwW/JP8k/wO/Q79gv2C/fL98v1h/mH+0v7S/kL/Qv+u/67/EQARAGgAaAC4ALgABwEHAVcBVwGrAasBAAIAAlICUgKeAp4C5gLmAigDKANkA2QDmgOaA8UDxQPlA+UD/gP+Aw8EDwQaBBoEIQQhBCAEIAQYBBgECgQKBPUD9QPcA9wDwgPCA6QDpAOAA4ADWANYAykDKQP2AvYCwALAAocChwJIAkgCAAIAAq8BrwFTAVMB8gDyAJIAkgA4ADgA5P/k/5j/mP9R/1H/Cv8K/8X+xf5//n/+Ov46/vn9+f28/bz9gv2C/U/9T/0j/SP9//z//OX85fzV/NX8zPzM/M38zfzY/Nj87Pzs/A39Df05/Tn9bv1u/az9rP3x/fH9OP44/n7+fv6//r/+9v72/iH/If9B/0H/V/9X/2b/Zv90/3T/gP+A/47/jv+d/53/rv+u/8H/wf/X/9f/8f/x/w4ADgAsACwASwBLAGkAaQCHAIcApACkAL4AvgDTANMA4QDhAOcA5wDoAOgA6ADoAOgA6ADpAOkA7QDtAPIA8gD4APgA/wD/AAcBBwELAQsBCAEIAfoA+gDdAN0AsgCyAHoAegA5ADkA8v/y/6j/qP9c/1z/DP8M/7r+uv5m/mb+DP4M/q/9r/1I/Uj91PzU/FP8U/y/+7/7FvsW+1v6W/qF+YX5jPiM+Hb3dvc89jz24PTg9Hvze/MX8hfywfDB8JXvle+X7pfuwe3B7R/tH+2k7KTsP+w/7Pnr+evD68PrjuuO62XrZes76zvrAOsA68XqxeqF6oXqPuo+6g3qDer26fbp9un26R/qH+pn6mfqvOq86ijrKOuo66jrN+w37OTs5Oyn7afta+5r7i3vLe/c79zvbfBt8PHw8fBz8XPx9vH28YryivIt8y3z1PPU84b0hvRH9Uf1GPYY9gX3BfcP+A/4Lvku+Vv6W/qM+4z7t/y3/Nb91v3t/u3+/f/9/wkBCQEYAhgCLgMuA0UERQRkBWQFjQaNBrsHuwf5CPkIUgpSCroLugsxDTENsg6yDhsQGxBmEWYRkxKTEpETkRNuFG4UOxU7FegV6BV7FnsW/Bb8FloXWheiF6IX5xfnFx0YHRhSGFIYjhiOGLkYuRjTGNMY5hjmGN4Y3hjEGMQYpxinGHUYdRgyGDIY5xfnF4AXgBcFFwUXhRaFFvQV9BVeFV4V0hTSFEIUQhS2E7YTNxM3E7YSthI2EjYSvBG8EToROhGyELIQKxArEJUPlQ/yDvIORA5EDn8Nfw2lDKUMwQvBC9YK1grrCesJDgkOCToIOghuB24HrAasBu4F7gU0BTQFhASEBNwD3AM6AzoDnQKdAgACAAJiAWIBxADEACsAKwCY/5j/EP8Q/5T+lP4j/iP+vP28/WD9YP0M/Qz9vfy9/Hn8efw+/D78D/wP/PL78vvl++X74/vj++f75/vo++j74fvh+9T71PvE+8T7tPu0+6j7qPug+6D7lvuW+4v7i/t++377bftt+2H7Yftb+1v7WftZ+1z7XPtf+1/7XPtc+1X7VftJ+0n7Ofs5+yv7K/sf+x/7EvsS+wf7B/v7+vv67Prs+uD64Prb+tv62vra+uD64Prr+uv68vry+vn6+fr++v76A/sD+xD7EPsp+yn7S/tL+3r7evux+7H76fvp+yP8I/xe/F78mPyY/Nj82Pwf/R/9bP1s/b79vv0R/hH+X/5f/qT+pP7j/uP+IP8g/2H/Yf+o/6j/9P/0/0AAQACFAIUAwwDDAPkA+QAtAS0BYwFjAZoBmgHPAc8B+wH7ARkCGQIqAioCMwIzAjgCOAI9Aj0CQwJDAkQCRAI+Aj4CMgIyAiMCIwITAhMCCAIIAv0B/QHyAfIB4gHiAcoBygGrAasBiAGIAWIBYgE9AT0BGgEaAfkA+QDaANoAvAC8AJ4AngB9AH0AWgBaADcANwAWABYA+//7/+b/5v/U/9T/w//D/67/rv+U/5T/eP94/17/Xv9I/0j/N/83/yr/Kv8d/x3/Df8N//v++/7r/uv+3v7e/tj+2P7X/tf+2P7Y/tr+2v7a/tr+1/7X/tL+0v7K/sr+v/6//rL+sv6e/p7+g/6D/mL+Yv47/jv+Dv4O/uH94f21/bX9i/2L/Wf9Z/1I/Uj9Lv0u/Rz9HP0Q/RD9DP0M/Q/9D/0a/Rr9Kv0q/T/9P/1X/Vf9cf1x/Yz9jP2q/ar9yP3I/ef95/0G/gb+I/4j/kD+QP5b/lv+dv52/pT+lP63/rf+4P7g/g//D/9A/0D/cP9w/5r/mv+8/7z/1f/V/+b/5v/x//H/9v/2//X/9f/t/+3/3//f/8z/zP+3/7f/o/+j/5H/kf9+/37/Zf9l/0P/Q/8S/xL/z/7P/nz+fP4Y/hj+oP2g/Rb9Fv1y/HL8rvuu+9P60/rg+eD53vje+OD34Pfu9u72C/YL9kT1RPWU9JT08fPx82bzZvPq8urydfJ18hLyEvK28bbxVfFV8fnw+fCX8JfwKvAq8MTvxO9m72bvE+8T79/u3+7N7s3u1e7V7gHvAe9J70nvn++f7wvwC/CI8IjwDvEO8aTxpPFB8kHy1fLV8mHzYfPe897zSPRI9LD0sPQc9Rz1jfWN9Qr2CvaQ9pD2FfcV95/3n/cy+DL40fjR+IX5hflM+kz6G/sb++n76fut/K38Yv1i/Qn+Cf6r/qv+Tf9N/+//7/+UAJQAOgE6Ad8B3wGHAocCOgM6A/gD+APJBMkEsQWxBaMGowaZB5kHjQiNCGsJawk0CjQK7ArsCo0LjQseDB4MqAyoDB8NHw2HDYcN5g3mDTIOMg55DnkOxw7HDhcPFw9vD28P1A/UDzIQMhCKEIoQ3hDeECARIBFXEVcRjBGMEa4RrhG/Eb8RxBHEEa4RrhGDEYMRVBFUERoRGhHhEOEQtRC1EIcQhxBbEFsQNxA3EA8QDxDoD+gPyA/ID6IPog91D3UPRA9ED/0O/Q6hDqEONQ41DrANsA0aDRoNfwx/DOAL4AtFC0ULugq6CjcKNwq8CbwJTQlNCeEI4Qh7CHsIHwgfCMQHxAdlB2UH/wb/BogGiAYBBgEGcgVyBd4E3gRLBEsEuwO7Ay4DLgOhAqECGAIYApcBlwEhASEBtwC3AFkAWQACAAIAsv+y/2j/aP8j/yP/4f7h/qL+ov5f/l/+Ff4V/sf9x/11/XX9Iv0i/dD80Px//H/8Lvwu/OP74/uk+6T7dvt2+1/7X/tZ+1n7W/tb+1z7XPtU+1T7P/s/+yX7JfsK+wr77Prs+s76zvqs+qz6f/p/+k36Tfoa+hr66vnq+cn5yfm3+bf5svmy+bv5u/nN+c354vni+f75/vke+h76QPpA+mj6aPqT+pP6uvq6+uH64foC+wL7Hfsd+zn7OftX+1f7evt6+6b7pvva+9r7EfwR/E/8T/yT/JP83vze/DH9Mf2K/Yr94v3i/TT+NP57/nv+tf61/uX+5f4R/xH/Pv8+/27/bv+h/6H/1f/V/wYABgA4ADgAbABsAKUApQDjAOMAJgEmAWgBaAGjAaMB1AHUAfQB9AECAgICAAIAAvAB8AHXAdcBvAG8AaQBpAGRAZEBhgGGAX8BfwF8AXwBgAGAAYkBiQGYAZgBrQGtAcIBwgHRAdEB1gHWAc4BzgG7AbsBowGjAYcBhwFrAWsBTgFOATABMAETARMB/QD9APEA8QDyAPIAAgECARwBHAE7ATsBWwFbAXgBeAGSAZIBpwGnAbUBtQG6AboBtQG1AaYBpgGOAY4BcgFyAVUBVQE8ATwBLgEuASoBKgEyATIBRQFFAV0BXQF1AXUBiAGIAZEBkQGPAY8BgwGDAW4BbgFPAU8BKgEqAf0A/QDMAMwAmQCZAGgAaAA9AD0AGQAZAPv/+//h/+H/yv/K/7X/tf+h/6H/jP+M/3b/dv9c/1z/Pv8+/xz/HP/2/vb+y/7L/qD+oP52/nb+T/5P/jH+Mf4b/hv+D/4P/g3+Df4Q/hD+Fv4W/h7+Hv4m/ib+K/4r/i7+Lv4q/ir+G/4b/gP+A/7i/eL9t/23/Yb9hv1Q/VD9E/0T/dT81PyX/Jf8XPxc/Cv8K/wA/AD81/vX+637rft8+3z7PPs8+/P68/qb+pv6L/ov+rP5s/kh+SH5c/hz+LX3tffq9ur2F/YX9k/1T/WY9Jj08fPx82fzZ/P08vTykPKQ8kXyRfIL8gvy2/Hb8b/xv/Gt8a3xmPGY8YPxg/Fk8WTxM/Ez8QDxAPHR8NHwq/Cr8KLwovC18LXw2fDZ8BfxF/Fl8WXxvfG98SjyKPKj8qPyIvMi86bzpvMi9CL0iPSI9OH04fQu9S71b/Vv9bT1tPX99f31RPZE9pH2kfbj9uP2Pfc996v3q/cz+DP40fjR+IP5g/k++j769/r3+qn7qftS/FL88Pzw/IX9hf0T/hP+mv6a/hz/HP+f/5//KQApALwAvABeAV4BFAIUAtwC3AK4A7gDqQSpBKMFowWiBqIGnQedB4IIgghTCVMJEgoSCrkKuQpRC1EL4AvgC1wMXAzKDMoMMw0zDZENkQ3uDe4NWA5YDsQOxA42DzYPrw+vDxwQHBB8EHwQ1BDUEBERERE6EToRVhFWEVgRWBFEEUQRJxEnEfMQ8xC0ELQQeBB4EDkQORAAEAAQ2Q/ZD7YPtg+bD5sPiQ+JD3MPcw9aD1oPQw9DDyEPIQ/1DvUOxA7EDn8Ofw4qDioOzw3PDWYNZg33DPcMiwyLDBwMHAyyC7ILVQtVC/8K/wqyCrIKcQpxCi8KLwroCegJnwmfCUwJTAnyCPIIlQiVCDAIMAjBB8EHSgdKB8cGxwY7BjsGsAWwBScFJwWhBKEEIgQiBKMDowMlAyUDqQKpAjECMQK/Ab8BUgFSAeUA5QByAHIA8v/y/2n/af/Z/tn+R/5H/rr9uv00/TT9svyy/Dj8OPzD+8P7VPtU+/P68/qf+p/6V/pX+h76Hvrx+fH5zPnM+bD5sPmW+Zb5dvl2+VD5UPkf+R/54/jj+Kv4q/h6+Hr4T/hP+C/4L/gT+BP49vf29+D34PfV99X32PfY9/L38vcd+B34TfhN+H/4f/iw+LD43Pjc+A35Dfk/+T/5bflt+Zf5l/m3+bf5zvnO+ef55/kL+gv6Ovo6+nn6efq/+r/6BfsF+0j7SPuM+4z70vvS+yH8Ifx2/Hb8y/zL/Bz9HP1l/WX9pv2m/eT95P0j/iP+ZP5k/qn+qf7x/vH+O/87/4f/h//Z/9n/LgAuAIYAhgDdAN0AMwEzAYcBhwHXAdcBJQIlAmsCawKnAqcC1QLVAvYC9gIOAw4DJQMlAz0DPQNXA1cDcwNzA4oDigOgA6ADugO6A9gD2AP9A/0DJgQmBEgESARfBF8EaQRpBGYEZgRaBFoETARMBDgEOAQeBB4E/AP8A9ED0QOhA6EDdAN0A0oDSgMlAyUDBAMEA+MC4wLBAsECoQKhAoACgAJeAl4COgI6Ag8CDwLfAd8BrQGtAXsBewFNAU0BIgEiAfgA+ADQANAAqwCrAI0AjQB3AHcAbABsAGkAaQBtAG0AdgB2AIYAhgCaAJoAsgCyAMgAyADWANYA2ADYAMwAzAC2ALYAmQCZAHkAeQBVAFUALwAvAAcABwDf/9//uf+5/5f/l/96/3r/Yv9i/07/Tv88/zz/K/8r/x3/Hf8O/w7//P78/uf+5/7O/s7+sP6w/pD+kP5v/m/+Tf5N/iv+K/4J/gn+5v3m/cj9yP2u/a79nP2c/ZP9k/2T/ZP9lv2W/Zr9mv2a/Zr9kP2Q/X79fv1l/WX9RP1E/SH9If37/Pv80fzR/Kb8pvx6/Hr8UPxQ/C78LvwS/BL8+fv5+9773vu7+7v7iPuI+0j7SPv4+vj6lfqV+iX6Jfqh+aH5A/kD+VT4VPiU95T3xvbG9gH2AfZK9Ur1pfSl9CH0IfS487jzY/Nj8ybzJvP88vzy2/Lb8tDy0PLT8tPy1/LX8uDy4PLf8t/yx/LH8qTypPJ58nnyS/JL8jLyMvIu8i7yOfI58lvyW/KP8o/yz/LP8inzKfOb85vzHPQc9K30rfQ/9T/1w/XD9Tr2Ovai9qL2+Pb49kb3RveL94v3xPfE9/n3+fcu+C74Zfhl+Kz4rPgF+QX5bvlu+ef55/lv+m/6/vr++pX7lfsx/DH8zPzM/GL9Yv3u/e79bf5t/uT+5P5Z/1n/0v/S/1EAUQDXANcAZgFmAfkB+QGaApoCUQNRAxwEHAT9BP0E7gXuBdoG2ga6B7oHiwiLCEYJRgn1CfUJoAqgCjsLOwvIC8gLSgxKDLQMtAwTDRMNcw1zDc8Nzw0vDi8OmQ6ZDv0O/Q5eD14PwQ/BDxUQFRBbEFsQlhCWELcQtxDAEMAQuhC6EJsQmxBqEGoQMBAwEOQP5A+SD5IPRw9HD/4O/g7ADsAOlA6UDmkOaQ5ADkAOHg4eDvcN9w3RDdENsQ2xDYgNiA1TDVMNFQ0VDcMMwwxjDGMMAQwBDJkLmQsvCy8LywrLCmYKZgoFCgUKrwmvCV4JXgkUCRQJ0QjRCIwIjAhFCEUI/Af8B6gHqAdHB0cH2QbZBlgGWAbGBcYFLQUtBY8EjwTzA/MDWgNaA8QCxAIuAi4CnAGcARIBEgGTAJMAHgAeALP/s/9J/0n/2/7b/mr+av7y/fL9d/13/f38/fyB/IH8AvwC/IT7hPsJ+wn7lPqU+jD6MPre+d75mvma+Wf5Z/k++T75G/kb+QP5A/n1+PX47Pjs+Ov46/jq+Or43vje+Mv4y/iu+K74iPiI+Gb4ZvhM+Ez4N/g3+C74Lvgu+C74M/gz+EL4Qvhe+F74hfiF+Lv4u/j6+Pr4OPk4+XX5dfms+az52vna+Qj6CPo4+jj6afpp+p36nfrQ+tD6//r/+i77Lvtg+2D7mPuY+9z73Psq/Cr8ffx9/NH80fwk/ST9c/1z/cH9wf0Q/hD+Xv5e/qv+q/7y/vL+NP80/3L/cv+x/7H/9P/0/z0APQCLAIsA3QDdACwBLAF7AXsByAHIARECEQJXAlcCmAKYAs4CzgL6AvoCHwMfAzwDPANWA1YDcANwA4cDhwOdA50DtAO0A8kDyQPhA+ED/gP+AxwEHAQ6BDoEWARYBHEEcQSGBIYEmQSZBKYEpgStBK0ErgSuBKMEowSPBI8EcwRzBFAEUAQnBCcE/QP9A9ED0QOlA6UDewN7A1IDUgMqAyoDBgMGA+MC4wLBAsECngKeAngCeAJOAk4CIgIiAvMB8wHDAcMBlAGUAWUBZQE2ATYBCQEJAd8A3wC7ALsAoACgAI0AjQCCAIIAgACAAIUAhQCRAJEAowCjALoAugDUANQA7gDuAAQBBAEXARcBJgEmATEBMQE3ATcBNwE3AS4BLgEcARwBBgEGAe0A7QDSANIAtwC3AJcAlwBxAHEARgBGABcAFwDq/+r/v/+//5b/lv9q/2r/Nv82//r++v64/rj+c/5z/jL+Mv72/fb9u/27/YL9gv1J/Un9D/0P/dz83Pyx/LH8jvyO/HT8dPxe/F78SvxK/Dv8O/wv/C/8Jvwm/B/8H/wZ/Bn8EPwQ/AX8Bfz5+/n77Pvs++H74fvW+9b7yfvJ+777vvuz+7P7p/un+5/7n/uW+5b7iPuI+3b7dvta+1r7Mfsx+/36/fq6+rr6Y/pj+vz5/PmB+YH58/jz+F34XfjD98P3J/cn95v2m/Yf9h/2svWy9V31XfUZ9Rn13vTe9LP0s/SS9JL0dfR19GT0ZPRX9Ff0Q/RD9C70LvQO9A704fPh87bztvOT85PzefN583bzdvOG84bzn/Of88jzyPP+8/7zPvQ+9JT0lPT/9P/0c/Vz9e/17/Vk9mT2yPbI9h33Hfdm92b3o/ej99/33/cZ+Bn4TPhM+H74fviw+LD45fjl+Cn5Kfl++X754/nj+Vj6WPra+tr6YPtg++n76ftw/HD88/zz/HP9c/3x/fH9bv5u/uz+7P5t/23/8P/w/3YAdgABAQEBlwGXATkCOQLtAu0CtQO1A4UEhQRaBVoFLQYtBvQG9AavB68HYwhjCAcJBwmfCZ8JLgouCqoKqgoYCxgLgAuAC9oL2gsvDC8MiQyJDN8M3ww5DTkNmQ2ZDfEN8Q0+Dj4OgQ6BDq8Orw7MDswO4w7jDukO6Q7gDuAOzw7PDqgOqA5zDnMOQA5ADgoOCg7aDdoNtg22DZENkQ1sDWwNTQ1NDS0NLQ0TDRMNBA0EDfIM8gzdDN0MxQzFDJ4MngxsDGwMNQw1DPML8wuoC6gLXAtcCwoLCgu3CrcKagpqCh0KHQrQCdAJhwmHCTkJOQnnCOcIlAiUCDkIOQjSB9IHYwdjB+cG5wZfBl8G0gXSBT8FPwWoBKgEEQQRBHgDeAPfAt8CSQJJArgBuAEsASwBpwCnACgAKACx/7H/Qf9B/9n+2f53/nf+F/4X/rn9uf1c/Vz9/fz9/KH8ofxH/Ef87/vv+5z7nPtP+0/7CvsK+9P60/qp+qn6ivqK+nb6dvpm+mb6VfpV+kb6Rvo5+jn6L/ov+i76Lvoy+jL6OPo4+j/6P/pE+kT6Q/pD+kP6Q/pE+kT6RfpF+kv6S/pS+lL6VfpV+lf6V/pW+lb6U/pT+lX6Vfpc+lz6Z/pn+nj6ePqK+or6m/qb+q/6r/rI+sj65/rn+hH7EftF+0X7fvt++737vfsA/AD8RPxE/Iz8jPzY/Nj8Jf0l/XL9cv28/bz9Af4B/kH+Qf6A/oD+wP7A/gT/BP9M/0z/l/+X/+L/4v8tAC0AegB6AMgAyAAbARsBcwFzAckByQEbAhsCZQJlAqQCpALYAtgCBgMGAywDLANMA0wDZwNnA3wDfAOMA4wDnAOcA6oDqgO3A7cDxAPEA80DzQPUA9QD2gPaA90D3QPfA98D3QPdA9QD1APFA8UDswOzA54DngOJA4kDdQN1A10DXQNBA0EDIQMhAwADAAPgAuACxwLHArACsAKZApkCgAKAAmMCYwJDAkMCJAIkAgcCBwLsAewB1AHUAbwBvAGjAaMBigGKAXABcAFVAVUBPAE8ASUBJQEUARQBDAEMAQ0BDQEUARQBHAEcASABIAEfAR8BGwEbARcBFwEVARUBFAEUAREBEQELAQsBBAEEAf8A/wD/AP8ABgEGAQ0BDQEQARABCwELAf4A/gDtAO0A2wDbAMgAyACwALAAkgCSAGsAawA8ADwABwAHAM//z/+U/5T/U/9T/w//D//J/sn+hP6E/kX+Rf4L/gv+1f3V/aP9o/1x/XH9Qv1C/Rn9Gf32/Pb81/zX/Lr8uvyb/Jv8efx5/Fr8Wvw//D/8Kvwq/B38HfwV/BX8DfwN/An8CfwI/Aj8DPwM/Bb8Fvwn/Cf8Ofw5/E38Tfxe/F78aPxo/G38bfxq/Gr8X/xf/FT8VPxK/Er8QvxC/D78Pvw3/Df8KPwo/BH8Efzx+/H7x/vH+5b7lvtb+1v7D/sP+7b6tvpN+k361vnW+V35Xfnn+Of4c/hz+An4Cfip96n3TvdO9wL3AvfD9sP2jPaM9mH2YfY99j32GPYY9vz1/PXi9eL1xPXE9af1p/WE9YT1VfVV9Sn1KfUC9QL14/Tj9Nn02fTe9N706vTq9AD1APUd9R31PPU89Wf1Z/Wc9Zz10vXS9Qv2C/ZA9kD2avZq9pL2kva59rn23vbe9gn3Cfc29zb3Yvdi95X3lffN9833CvgK+FL4Uvim+Kb4AfkB+Wf5Z/nX+df5TfpN+sj6yPpF+0X7wPvA+zr8Ovy1/LX8Mv0y/bP9s/04/jj+wf7B/kv/S//c/9z/eAB4ACEBIQHbAdsBpQKlAnMDcwNDBEMEEAUQBdMF0wWQBpAGSgdKB/gH+AeeCJ4IPQk9CcoJyglLCksKxwrHCjYLNgueC54LCAwIDGoMagzKDMoMLA0sDYQNhA3TDdMNIA4gDl0OXQ6RDpEOww7DDuMO4w72DvYOAA8AD/UO9Q7fDt8Oyg7KDq8Orw6UDpQOfA58DlsOWw4xDjEOCQ4JDtoN2g2vDa8Njg2ODWwNbA1IDUgNJg0mDfkM+QzEDMQMjQyNDEkMSQz7C/sLpgumC0QLRAvYCtgKawprCvgJ+AmECYQJFQkVCacIpwg9CD0I2gfaB3kHeQcYBxgHtQa1BkwGTAbaBdoFYwVjBeUE5QRfBF8E1QPVA0UDRQOvAq8CGAIYAoYBhgH9AP0AgQCBABMAEwCy/7L/XP9c/w//D//I/sj+h/6H/kz+TP4U/hT+2v3a/Z/9n/1g/WD9Gv0a/dL80vyK/Ir8Q/xD/AH8AfzG+8b7kfuR+2j7aPtJ+0n7M/sz+yX7Jfsc+xz7E/sT+w77DvsM+wz7DPsM+xD7EPsR+xH7CPsI+/f69/re+t76wPrA+qr6qvqc+pz6lPqU+pD6kPqJ+on6fPp8+nL6cvpx+nH6evp6+pL6kvqy+rL60frR+uz67PoE+wT7Gvsa+zb7Nvtb+1v7g/uD+6z7rPvS+9L79Pv0+xn8GfxL/Ev8jfyN/OP84/xH/Uf9r/2v/RT+FP51/nX+0f7R/i7/Lv+M/4z/6f/p/z8APwCIAIgAxQDFAPgA+AAnAScBVwFXAYYBhgG0AbQB4AHgAQkCCQIyAjICXwJfApACkALDAsMC+AL4AigDKANRA1EDdQN1A44DjgObA5sDmwObA40DjQN0A3QDVwNXAzkDOQMaAxoD/QL9At4C3gLBAsECqwKrAp4CngKcApwCpQKlArACsAK5ArkCvgK+ArwCvAK2ArYCrgKuAqICogKTApMCggKCAm8CbwJaAloCRQJFAisCKwIOAg4C8AHwAdQB1AG/Ab8BsAGwAaQBpAGWAZYBhAGEAXABcAFdAV0BTwFPAUQBRAE2ATYBHwEfAf4A/gDVANUArgCuAI0AjQBzAHMAXQBdAEUARQAqACoADgAOAPX/9f/i/+L/0v/S/8T/xP+y/7L/nv+e/4n/if91/3X/Y/9j/0//T/82/zb/Ff8V//D+8P7J/sn+pP6k/oP+g/5k/mT+RP5E/iT+JP4G/gb+6v3q/dT91P3D/cP9sv2y/aL9ov2R/ZH9ff19/Wn9af1T/VP9O/07/SP9I/0N/Q39+fz5/Ov86/zf/N/81PzU/Mz8zPzK/Mr8z/zP/N/83/z3/Pf8Ef0R/Sn9Kf09/T39Tv1O/V/9X/1y/XL9hP2E/ZL9kv2Z/Zn9l/2X/ZH9kf2K/Yr9hf2F/YX9hf2H/Yf9iP2I/Yf9h/2D/YP9ev16/W39bf1Y/Vj9Nv02/Qr9Cv3P/M/8hvyG/DL8MvzS+9L7Zvtm+/X69fqB+oH6D/oP+qn5qflP+U/5//j/+L/4v/iJ+In4WPhY+DL4MvgQ+BD47/fv99H30few97D3hfeF91P3U/ca9xr32PbY9pz2nPZo9mj2OfY59hj2GPYA9gD27fXt9ev16/X69fr1GPYY9kj2SPaA9oD2sPaw9tr22vb89vz2F/cX9zj3OPdc91z3ffd995r3mvew97D3wPfA99j32Pf99/33Mfgx+HT4dPjB+MH4EPkQ+WX5ZfnB+cH5JPok+pH6kfoD+wP7cvty+9773vtE/ET8pvym/An9Cf1x/XH93v3e/VH+Uf7O/s7+Vv9W/+n/6f+KAIoAOAE4AewB7AGoAqgCZgNmAx8EHwTQBNAEegV6BRMGEwahBqEGJgcmB5sHmwcFCAUIaghqCMUIxQggCSAJhgmGCfAJ8AlfCl8K1grWCkMLQwuqC6oLEAwQDGwMbAzCDMIMEw0TDU4NTg1xDXENgw2DDYANgA1yDXINZQ1lDU0NTQ0rDSsNBQ0FDdYM1gypDKkMigyKDHAMcAxaDFoMRQxFDCIMIgz1C/ULxwvHC5MLkwtbC1sLIAsgC9cK1wp/Cn8KJAokCsQJxAloCWgJEwkTCbsIuwhgCGAIBwgHCK8HrwdiB2IHJAckB+0G7Qa2BrYGewZ7BjUGNQboBegFmAWYBUMFQwXmBOYEgQSBBBIEEgSgA6ADNQM1A9YC1gKGAoYCQgJCAgQCBALHAccBjAGMAVMBUwEcARwB6QDpALUAtQB9AH0APwA/APv/+/+y/7L/Y/9j/w//D/+4/rj+Xv5e/gj+CP65/bn9cv1y/TX9Nf3//P/8zvzO/Kb8pvyH/If8cfxx/GH8YfxT/FP8PPw8/CD8IPz9+/371vvW+7T7tPuV+5X7ePt4+177XvtI+0j7Nfs1+y77Lvs0+zT7QvtC+1n7Wftx+3H7iPuI+6H7ofu9+7372vva+/j7+PsQ/BD8Hvwe/CX8Jfwp/Cn8MPww/EP8Q/xk/GT8kfyR/Mb8xvwA/QD9Ov06/Xf9d/23/bf9+f35/Tv+O/56/nr+sP6w/t3+3f4D/wP/JP8k/0b/Rv9s/2z/lf+V/8D/wP/u/+7/HQAdAFAAUACJAIkAygDKABABEAFZAVkBnwGfAd0B3QERAhECPgI+AmECYQJ/An8CmAKYAqkCqQK2ArYCwQLBAs0CzQLdAt0C8wLzAggDCAMbAxsDKAMoAy4DLgMxAzEDNwM3AzsDOwM7AzsDMwMzAx4DHgMAAwAD4QLhAsYCxgKwArACnAKcAoICggJiAmICQAJAAiECIQIJAgkC9wH3AeEB4QHBAcEBlAGUAWABYAEsASwBAAEAAdwA3AC6ALoAlgCWAG8AbwBHAEcAJgAmAAwADAD1//X/3v/e/8H/wf+g/6D/gP+A/2b/Zv9T/1P/Q/9D/zT/NP8k/yT/Fv8W/w3/Df8I/wj/Bv8G/wL/Av/7/vv+9v72/vb+9v7+/v7+Cf8J/xH/Ef8Q/xD/BP8E//T+9P7n/uf+3v7e/tj+2P7M/sz+s/6z/pD+kP5p/mn+RP5E/ij+KP4Q/hD+8/3z/c/9z/2n/af9gP2A/WT9ZP1V/VX9Tv1O/Uv9S/1G/Ub9Pv0+/Tr9Ov07/Tv9P/0//UX9Rf1H/Uf9RP1E/UP9Q/1H/Uf9U/1T/Wf9Z/1//X/9l/2X/bD9sP3P/c/98/3z/R7+Hv5J/kn+bf5t/on+if6e/p7+sP6w/sT+xP7b/tv+8P7w/gH/Af8N/w3/GP8Y/yf/J/85/zn/Sf9J/1D/UP9H/0f/Kv8q//7+/v7H/sf+hv6G/j7+Pv7p/en9hP2E/RT9FP2d/J38JPwk/LP7s/tJ+0n74/rj+ob6hvox+jH64vni+Z75nvle+V75GfkZ+dT41PiL+Iv4Pfg9+PT39Pet9633Yfdh9xX3FffI9sj2evZ69jr2OvYJ9gn24fXh9cf1x/Wz9bP1ovWi9Z71nvWp9an1vPW89dz13PX/9f/1HfYd9j72PvZi9mL2hvaG9q72rvbQ9tD24vbi9u327fb49vj2CPcI9yz3LPdg92D3lveW99D30PcM+Az4TfhN+KL4ovgL+Qv5f/l/+fb59vll+mX6yfrJ+ir7KvuR+5H7//v/+3P8c/zp/On8Xf1d/dX91f1c/lz+9/73/qP/o/9WAFYABgEGAaoBqgFKAkoC7wLvApsDmwNMBEwE/AT8BJkFmQUgBiAGmgaaBgoHCgd8B3wH9gf2B24IbgjiCOIIUglSCbgJuAkaChoKgQqBCuMK4wpAC0ALmAuYC+AL4AsZDBkMSwxLDG4MbgyIDIgMngyeDKcMpwymDKYMoAygDI8Mjwx4DHgMZQxlDFAMUAw8DDwMLgwuDBwMHAwIDAgM+Qv5C+gL6AvYC9gLywvLC7QLtAuVC5ULcgtyC0gLSAseCx4L+Qr5Cs0KzQqZCpkKYQphCiAKIAriCeIJsQmxCYcJhwleCV4JMgkyCfcI9wivCK8IZQhlCBoIGgjPB88HhQeFBy4HLgfKBsoGXwZfBvAF8AWCBYIFGgUaBbAEsARABEAEzQPNA1oDWgPwAvACkwKTAj4CPgLqAeoBkgGSATQBNAHVANUAewB7ACgAKADZ/9n/if+J/zf/N//i/uL+j/6P/kT+RP7+/f79uv26/Xn9ef03/Tf99vz2/Lv8u/yE/IT8UPxQ/B78Hvzt++37vvu++5b7lvt3+3f7XPtc+0b7Rvsw+zD7GPsY+wL7Avvx+vH64Prg+tP60/rD+sP6rfqt+pr6mvqL+ov6gvqC+oT6hPqO+o76mvqa+qv6q/rC+sL63vre+gX7Bfs1+zX7Zvtm+5n7mfvN+837AfwB/Dn8Ofx0/HT8rvyu/Oj86Pwh/SH9W/1b/Zv9m/3j/eP9L/4v/nz+fP7G/sb+Df8N/1T/VP+e/57/7f/t/zsAOwCCAIIAvAC8AOwA7AAXARcBRQFFAXcBdwGqAaoB2QHZAQACAAIfAh8CPAI8AlkCWQJ3AncClAKUAqkCqQKyArICtAK0Aq8CrwKmAqYCmgKaAokCiQJ2AnYCZQJlAloCWgJZAlkCYAJgAmUCZQJjAmMCWQJZAkoCSgI9Aj0CNwI3AjMCMwIsAiwCHwIfAgcCBwLrAesB0AHQAbcBtwGeAZ4BggGCAWABYAE7ATsBGQEZAf8A/wDrAOsA2wDbAMkAyQCzALMAmwCbAIYAhgB0AHQAYwBjAE4ATgAyADIADwAPAOz/7P/P/8//uf+5/6r/qv+d/53/jv+O/37/fv9u/27/Yf9h/1n/Wf9V/1X/Uf9R/03/Tf9L/0v/Tf9N/1T/VP9g/2D/bP9s/3f/d/9+/37/gf+B/4T/hP+H/4f/iv+K/4n/if+A/4D/bP9s/0//T/8s/yz/Bv8G/+P+4/7B/sH+oP6g/oD+gP5f/l/+P/4//iT+JP4P/g/+AP4A/vT99P3o/ej92P3Y/cb9xv2z/bP9oP2g/ZP9k/2L/Yv9hv2G/YX9hf2J/Yn9kf2R/Z79nv2t/a39u/27/cj9yP3W/db96P3o/QT+BP4o/ij+T/5P/nT+dP6U/pT+sf6x/tD+0P72/vb+Iv8i/03/Tf9w/3D/h/+H/5X/lf+g/6D/rP+s/7j/uP+9/73/tf+1/5z/nP93/3f/Sv9K/xn/Gf/h/uH+m/6b/kP+Q/7Z/dn9YP1g/eX85fxr/Gv88fvx+3r7evsE+wT7jfqN+h36Hfq3+bf5VvlW+QD5APmu+K74WPhY+AT4BPir96v3RvdG99723vZx9nH2AvYC9qH1ofVN9U31/fT99Lj0uPR29Hb0NfQ19Aj0CPTy8/Lz7/Pv8wL0AvQb9Bv0LfQt9EH0QfRb9Fv0fvR+9LT0tPTv9O/0IPUg9Ub1RvVh9WH1d/V39Zv1m/XP9c/1C/YL9lD2UPaV9pX22PbY9ij3KPeK94r3/Pf89334ffgB+QH5ffl9+e/57/la+lr6wvrC+i/7L/ul+6X7H/wf/Jv8m/wZ/Rn9mP2Y/R7+Hv6z/rP+W/9b/xMAEwDUANQAlAGUAUkCSQLxAvECkQORAycEJwS4BLgERQVFBcUFxQU7BjsGrwavBiEHIQeaB5oHIAggCKcIpwgsCSwJsgmyCTAKMAquCq4KMQsxC6sLqwsZDBkMeAx4DLwMvAzuDO4MGQ0ZDTkNOQ1RDVENZw1nDXANcA1yDXINew17DYINgg2MDYwNnQ2dDaQNpA2hDaENnA2cDYsNiw11DXUNXw1fDT0NPQ0RDREN5AzkDK4Mrgx3DHcMRgxGDBEMEQzcC9wLqwurC3cLdwtBC0ELDQsNC9EK0QqPCo8KTQpNCgcKBwrACcAJfQl9CTIJMgneCN4IgwiDCCAIIAi9B70HZQdlBxIHEgfABsAGagZqBgcGBwaYBZgFKAUoBbsEuwRVBFUE9gP2A5YDlgMwAzADxwLHAl0CXQL2AfYBlAGUATUBNQHUANQAbwBvAAoACgCr/6v/VP9U/wf/B/++/r7+df51/i3+Lf7q/er9sP2w/YH9gf1Z/Vn9Lf0t/fz8/PzE/MT8jPyM/Fz8XPw4/Dj8GPwY/Pj7+PvR+9H7o/uj+3v7e/tg+2D7VPtU+1b7Vvtc+1z7Wvta+1X7VftO+077SvtK+077TvtW+1b7Wvta+1f7V/tQ+1D7R/tH+0j7SPtU+1T7Z/tn+3/7f/uW+5b7qPuo+7z7vPvW+9b7+vv6+yr8Kvxh/GH8l/yX/Mn8yfz2/Pb8IP0g/U39Tf1//X/9tv22/e397f0g/iD+TP5M/nX+df6g/qD+0f7R/gr/Cv9H/0f/g/+D/7j/uP/n/+f/EwATAD8APwBrAGsAlQCVALgAuADSANIA5ADkAPMA8wACAQIBEgESASEBIQEsASwBNAE0ATgBOAE/AT8BSQFJAVUBVQFfAV8BZwFnAWoBagFtAW0BcQFxAXcBdwF7AXsBfAF8AXYBdgFrAWsBXwFfAVMBUwFJAUkBQgFCATwBPAE4ATgBNwE3ATsBOwFBAUEBRwFHAUsBSwFMAUwBTQFNAU8BTwFSAVIBUwFTAUwBTAE8ATwBJAEkAQUBBQHlAOUAxgDGAKgAqACIAIgAaABoAEkASQAuAC4AGgAaAAwADAADAAMA+f/5/+//7//l/+X/2v/a/9H/0f/H/8f/u/+7/67/rv+g/6D/mP+Y/5f/l/+f/5//r/+v/8X/xf/e/97/+//7/x0AHQBFAEUAcwBzAKIAogDOAM4A8QDxAAcBBwESARIBEwETAQ8BDwEHAQcB/wD/APUA9QDnAOcA1gDWAMIAwgCvAK8AngCeAJAAkACCAIIAcwBzAFwAXAA9AD0AFAAUAOX/5f+v/6//df91/zn/Of/8/vz+v/6//oT+hP5N/k3+Gf4Z/u797v3L/cv9r/2v/Zv9m/2M/Yz9e/17/Wr9av1Y/Vj9Sf1J/UD9QP1A/UD9RP1E/Ur9Sv1Q/VD9VP1U/V79Xv1v/W/9h/2H/aP9o/2+/b790v3S/eP94/3z/fP9BP4E/hb+Fv4k/iT+J/4n/h3+Hf4F/gX+4v3i/bv9u/2M/Yz9VP1U/RL9Ev3G/Mb8cfxx/Bz8HPzG+8b7a/tr+w37Dfuo+qj6Pvo++tj52Pl3+Xf5GfkZ+cD4wPhk+GT4AfgB+Jz3nPc09zT3xvbG9lz2XPby9fL1g/WD9Rr1GvW19LX0UPRQ9Pfz9/On86fzXPNc8yTzJPP68vry2PLY8sXyxfK78rvyrvKu8qryqvKq8qryqvKq8rHysfK58rnyuvK68r7yvvLI8sjy1/LX8vny+fIp8ynzXvNe857znvPl8+XzMPQw9Ij0iPTs9Oz0VPVU9cT1xPU69jr2s/az9jn3OffO9873bPhs+BP5E/nA+cD5bfpt+iH7Ifvh++H7r/yv/Iv9i/10/nT+ZP9k/1cAVwBSAVIBWAJYAmQDZAN6BHoElQWVBaoGqga6B7oHxwjHCMcJxwnACsAKuQu5C6UMpQyMDYwNdA50Dk8PTw8iECIQ9RD1ELoRuhF3EncSNhM2E+MT4xN+FH4UDRUNFXkVeRXLFcsVEBYQFjgWOBZMFkwWVRZVFj4WPhYOFg4W1RXVFYgViBUwFTAV2BTYFG4UbhTyE/ITaxNrE80SzRIgEiAScRFxEbUQtRDtD+0PHw8fD0EOQQ5XDVcNbQxtDH4LfguLCosKmwmbCakIqQi4B7gHzQbNBukF6QUMBQwFOAQ4BGkDaQOdAp0C1gHWARcBFwFgAGAAs/+z/xP/E/97/nv+6v3q/WH9Yf3c/Nz8Xfxd/Or76vuD+4P7KPso+9z63Pqg+qD6b/pv+k76Tvo++j76Ofo5+kT6RPpZ+ln6cvpy+pD6kPqy+rL61vrW+gD7APsv+y/7Yfth+5n7mfvX+9f7GfwZ/GL8Yvyw/LD8AP0A/VL9Uv2n/af9//3//Vr+Wv64/rj+Ev8S/2T/ZP+s/6z/6P/o/xoAGgBCAEIAYQBhAHQAdAB8AHwAeQB5AG8AbwBiAGIAVgBWAEsASwBAAEAANAA0ACQAJAARABEA+//7/+H/4f/B/8H/mf+Z/2n/af8x/zH/8f7x/q3+rf5m/mb+Hf4d/tf91/2Y/Zj9Yv1i/Tr9Ov0e/R79C/0L/QH9Af3+/P78Af0B/Q79Dv0i/SL9Ov06/VT9VP1w/XD9jP2M/a39rf3W/db9CP4I/kP+Q/6E/oT+yP7I/g7/Dv9X/1f/ov+i/+7/7v88ADwAiQCJANIA0gAWARYBWAFYAZQBlAHOAc4BAwIDAjECMQJZAlkCewJ7ApgCmAKxArECygLKAt8C3wLvAu8C+wL7AgEDAQMDAwMDBAMEAwEDAQP6AvoC7gLuAtsC2wK/Ar8CngKeAngCeAJPAk8CJQIlAvkB+QHLAcsBmgGaAWgBaAE2ATYBBwEHAd0A3QC4ALgAlgCWAHkAeQBdAF0ARABEACsAKwARABEA9P/0/9L/0v+r/6v/f/9//1H/Uf8k/yT/+f75/tL+0v6t/q3+i/6L/mz+bP5T/lP+Qf5B/jj+OP41/jX+N/43/jr+Ov4//j/+Rf5F/lD+UP5h/mH+dv52/o7+jv6n/qf+wf7B/t/+3/4E/wT/Mv8y/2j/aP+m/6b/6P/o/y4ALgB3AHcAxgDGABcBFwFqAWoBuwG7AQYCBgJKAkoCiQKJAsECwQL0AvQCIgMiA0YDRgNgA2ADdAN0A4ADgAOIA4gDjgOOA44DjgOGA4YDdwN3A10DXQM5AzkDDwMPA9sC2wKfAp8CWAJYAggCCAKsAawBSgFKAeIA4gB3AHcACgAKAJ7/nv8y/zL/x/7H/mL+Yv4C/gL+qP2o/Vf9V/0M/Qz9x/zH/If8h/xL/Ev8EvwS/N/73/u0+7T7jfuN+2/7b/tY+1j7RftF+zv7O/s3+zf7Ofs5+0T7RPtV+1X7aPto+377fvuU+5T7p/un+7r7uvvH+8f7y/vL+8b7xvu1+7X7lfuV+2z7bPs4+zj79/r3+q/6r/pd+l36AfoB+qT5pPlD+UP53Pjc+HT4dPgG+Ab4i/eL9w73DveK9or2/vX+9XT1dPXk9OT0SPRI9KrzqvMG8wbzWvJa8rjxuPEc8RzxgPCA8PDv8O9n72fv3+7f7mnuae4E7gTupu2m7WDtYO0o7Sjt8+zz7NHs0ey77Lvsquyq7K3srey97L3szuzO7PDs8Owh7SHtW+1b7a7tru0W7hbuiu6K7hXvFe+077TvYfBh8CbxJvEA8gDy5vLm8tnz2fPW9Nb01/XX9eD24Pb29/b3FPkU+T36Pfpx+3H7sPyw/Pf99/1O/07/tAC0ACMCIwKdA50DIQUhBaIGogYlCCUIrAmsCSoLKguhDKEMFA4UDnIPcg/CEMIQDRINEkETQRNnFGcUhRWFFYgWiBZ4F3gXYhhiGDEZMRnvGe8ZpRqlGjsbOxu3G7cbJRwlHHEccRyjHKMcyhzKHM0czRyzHLMchxyHHDYcNhzIG8gbUBtQG7wavBoTGhMaYRlhGZUYlRi3F7cX1RbVFuQV5BXqFOoU8RPxE+4S7hLkEeQR3BDcEM4Pzg+8DrwOrQ2tDZsMmwyIC4gLewp7CnAJcAlqCGoIbgduB3cGdwaIBYgFogSiBMcDxwP3AvcCNQI1AoABgAHWANYANQA1AJ7/nv8N/w3/g/6D/v/9//2B/YH9BP0E/Y/8j/wd/B38rfut+0P7Q/vb+tv6dfp1+hf6F/q/+b/5bvlu+Sn5Kfnq+Or4rPis+HP4c/g9+D34BvgG+Nf31/er96v3fPd89073Tvcd9x335vbm9rT2tPaH9of2XPZc9jv2O/Yh9iH2CfYJ9vr1+vX19fX19/X39Qb2BvYe9h72OvY69l72XvaH9of2sfax9uL24vYZ9xn3UvdS95L3kvfZ99n3Ifgh+HL4cvjJ+Mn4I/kj+Yf5h/nz+fP5Zfpl+t/63/ph+2H75vvm+278bvz6/Pr8h/2H/Rb+Fv6m/qb+Nf81/8L/wv9PAE8A2wDbAGYBZgHxAfEBfQJ9AgUDBQOKA4oDDwQPBI4EjgQKBQoFhAWEBfcF9wVlBmUG0AbQBjEHMQeJB4kH2gfaBx4IHghXCFcIiwiLCLQItAjVCNUI8QjxCAAJAAkFCQUJBAkECfgI+AjkCOQIzQjNCKoIqgh/CH8ITghOCBEIEQjKB8oHfAd8ByEHIQe8BrwGTgZOBtYF1gVWBVYF0gTSBEkESQS9A70DMAMwA6ICogIVAhUCiwGLAQQBBAF/AH8A+//7/3r/ev/6/vr+fP58/gP+A/6P/Y/9H/0f/bb8tvxU/FT89/v3+6P7o/tZ+1n7FfsV+9/63/q0+rT6lfqV+oP6g/p++n76gfqB+pH6kfqu+q761PrU+gf7B/tD+0P7gvuC+8b7xvsN/A38WfxZ/Kv8q/wF/QX9Y/1j/cL9wv0h/iH+f/5//t7+3v5B/0H/qP+o/w8ADwBzAHMA0wDTACwBLAGAAYAB0gHSAR4CHgJlAmUCpAKkAtkC2QIEAwQDKQMpA0gDSANiA2IDeAN4A4kDiQOVA5UDngOeA6UDpQOoA6gDqQOpA6UDpQOcA5wDkQORA4QDhAN1A3UDZANkA00DTQMuAy4DCgMKA+AC4AK2ArYCjAKMAmECYQIzAjMCAgICAs0BzQGXAZcBZAFkATIBMgEAAQABzADMAJUAlQBXAFcAFQAVAND/0P+K/4r/Q/9D//v++/6z/rP+Z/5n/h3+Hf7T/dP9iP2I/UH9Qf39/P38uvy6/Hz8fPxB/EH8CfwJ/Nj72Put+637hvuG+2b7ZvtM+0z7Nfs1+yT7JPsZ+xn7EfsR+xH7EfsY+xj7Ivsi+zH7MftD+0P7VPtU+2n7afuA+4D7l/uX+7L7svvO+8775/vn+wH8AfwX/Bf8Jvwm/Cz8LPwm/Cb8DvwO/Oj76Pux+7H7aPto+xH7Efuq+qr6Lvou+qb5pvkQ+RD5a/hr+MD3wPcK9wr3RfZF9nr1evWl9KX0wvPC8+Dy4PL58fnxBfEF8RPwE/Aa7xrvFO4U7hjtGO0f7B/sJOsk6zvqO+pe6V7pg+iD6MLnwucX5xfnduZ25vjl+OWU5ZTlO+U75QflB+Xv5O/k6OTo5AjlCOVH5UflleWV5QXmBeaS5pLmMucy5/fn9+fe6N7o2enZ6fXq9eou7C7seu167eTu5O5s8GzwDfIN8sjzyPOZ9Zn1d/d39175XvlP+0/7R/1H/UT/RP9KAUoBVwNXA18FXwVkB2QHaAloCV8LXwtTDVMNSw9LDzQRNBEUExQT7hTuFKkWqRZQGFAY7RntGWkbaRvLHMscHR4dHj4fPh85IDkgHiEeIdQh1CFrImsi9CL0IlAjUCOKI4ojsCOwI6kjqSODI4MjUCNQI/gi+CKCIoIi/iH+IVUhVSGSIJIgxh/GH94e3h7hHeEd2xzbHLwbvBuJGokaVBlUGRIYEhjJFskWhhWGFTwUPBTwEvASrRGtEWwQbBAyDzIPCA4IDuQM5AzHC8cLtwq3Cq4JrgmtCK0Iuge6B9EG0QbwBfAFGwUbBU4ETgSIA4gDzALMAhgCGAJrAWsBxQDFACcAJwCQ/5D//f79/nH+cf7n/ef9XP1c/dH80fxC/EL8rfut+xj7GPt/+n/63/nf+T75PvmY+Jj46vfq9z73PveO9o722PXY9Sn1KfV79Hv0zPPM8yrzKvOP8o/y9/H38W3xbfHu8O7wcfBx8APwA/Cf75/vPu8+7+/u7+6u7q7udu527lTuVO5E7kTuP+4/7lTuVO5/7n/uu+677hTvFO+F74XvA/AD8JbwlvA78Tvx7PHs8bTytPKR85HzfvR+9Hv1e/WG9ob2lveW9674rvjP+c/5+Pr4+ij8KPxe/V79l/6X/s3/zf8EAQQBOwI7AmwDbAOZBJkEwQXBBdoG2gbnB+cH6gjqCNkJ2Qm4CrgKiwuLC0YMRgztDO0Mhg2GDQcOBw51DnUO2g7aDikPKQ9nD2cPmg+aD7YPtg+9D70PtQ+1D5EPkQ9WD1YPDA8MD6oOqg4zDjMOsA2wDRgNGA1wDHAMwgvCCwsLCwtQClAKlwmXCdsI2wgbCBsIXAdcB5wGnAbcBdwFIgUiBWsEawS2A7YDAwMDA1MCUwKmAaYBAQEBAWYAZgDW/9b/T/9P/9D+0P5X/lf+5P3k/Xv9e/0e/R79yvzK/H/8f/w5/Dn89vv2+7f7t/t9+337R/tH+xn7Gfvu+u76wfrB+pX6lfpn+mf6Nvo2+gf6B/rZ+dn5qPmo+Xn5eflJ+Un5FvkW+ev46/jE+MT4nvie+Hz4fPhb+Fv4M/gz+A74Dvjr9+v3y/fL97b3tveq96r3ovei96T3pPew97D3w/fD9+T35PcV+BX4UfhR+Jz4nPj0+PT4VflV+cX5xflD+kP6y/rL+l77Xvv5+/n7mvya/ED9QP3s/ez9nv6e/lT/VP8RABEA0QDRAJEBkQFSAlICEwMTA84DzgODBIMENQU1BdsF2wV4BngGEAcQB5oHmgcaCBoIkAiQCPQI9AhFCUUJiQmJCbgJuAnWCdYJ6gnqCe4J7gnmCeYJ1gnWCbYJtgmGCYYJTAlMCQEJAQmoCKgIRghGCNYH1gdXB1cHzQbNBjUGNQaQBZAF4wTjBC8ELwR0A3QDtgK2AvUB9QEyATIBbwBvALD/sP/y/vL+Nv42/n/9f/3L/Mv8GfwZ/HH7cfvQ+tD6M/oz+p75nvkQ+RD5hfiF+Af4B/iX95f3Mvcy9+D24Pae9p72ZvZm9kD2QPYq9ir2IvYi9jD2MPZP9k/2efZ59rP2s/b59vn2R/dH96L3ovcH+Af4cvhy+OP44/ha+Vr50vnS+U36TfrI+sj6QftB+7j7uPss/Cz8mfyZ/AL9Av1i/WL9tv22/fz9/P0u/i7+Sf5J/kz+TP4z/jP++/37/aX9pf0w/TD9nPyc/PD78Pst+y37U/pT+mv5a/lw+HD4YPdg90n2SfYm9Sb19PP088Pyw/KM8YzxSfBJ8AvvC+/K7crtf+x/7EDrQOsH6gfqzujO6K/nr+em5qbmquWq5dXk1eQf5B/kfuN+4wzjDOPD4sPil+KX4p/in+LR4tHiHuMe45rjmuNB5EHkB+UH5fzl/OUY5xjnSehJ6Jvpm+kI6wjriOyI7CjuKO7l7+Xvt/G38Z3znfOV9ZX1mfeZ96r5qvnM+8z7/P38/S0ALQBdAl0ChASEBJcGlwadCJ0ImwqbCoIMggxYDlgOHhAeEMIRwhFOE04T0RTRFDsWOxaXF5cX7RjtGCIaIho8GzwbSBxIHC8dLx3+Hf4dxB7EHmIfYh/eH94fSCBIIIUghSCmIKYgwiDCIMIgwiCwILAgnSCdIGwgbCApICkg6B/oH5Ifkh8yHzIf1x7XHmYeZh7mHeYdZR1lHdAc0BwyHDIcmhuaG/Ia8ho8GjwagRmBGbAYsBjQF9AX8hbyFgwWDBYkFSQVRBREFF0TXRNyEnISjBGMEaAQoBCuD64Pvg6+DsINwg26DLoMqwurC5IKkgpvCW8JSghKCB8HHwfsBewFswSzBHQDdAMxAjEC7QDtAK//r/90/nT+PP08/Qz8DPzg+uD6tfm1+ZL4kvhy93L3T/ZP9jP1M/UZ9Bn0/vL+8vPx8/H08PTw++/77xnvGe9G7kbufO187czszOwy7DLspeul6zjrOOvn6ufqqOqo6o3qjeqN6o3qn+qf6tHq0eoa6xrrbett69nr2etW7Fbs3Ozc7HjteO0n7ifu4e7h7rHvse+V8JXwhvGG8YnyifKc85zztvS29Nz13PUM9wz3P/g/+HX5dfmu+q765fvl+xX9Ff1B/kH+ZP9k/3sAewCJAYkBjgKOAoUDhQNzBHMEWgVaBTYGNgYLBwsH3AfcB6AIoAhbCVsJDQoNCqsKqwo5CzkLvQu9CywMLAyMDIwM4wzjDCUNJQ1XDVcNgA2ADZgNmA2mDaYNtQ21DboNug27DbsNvw2/DboNug2yDbINrQ2tDaANoA2LDYsNcw1zDUsNSw0UDRQN1QzVDIUMhQwpDCkMyQvJC1wLXAvmCuYKbQptCusJ6wlkCWQJ3gjeCFQIVAjFB8UHNgc2B6AGoAYFBgUGZgVmBcEEwQQTBBMEXANcA5kCmQLJAckB7ADsAAcABwAd/x3/Lv4u/kP9Q/1a/Fr8cftx+5H6kfq2+bb53/jf+BX4FfhS91L3kfaR9tz13PUu9S71gvSC9OTz5PNP80/zvfK98jjyOPK+8b7xSfFJ8enw6fCe8J7wZfBl8ErwSvBK8ErwXPBc8Ivwi/DS8NLwKvEq8Zvxm/Eh8iHys/Kz8lbzVvMI9Aj0xPTE9I/1j/Vn9mf2RfdF9y74Lvgh+SH5Hvoe+if7J/s9/D38XP1c/X7+fv6j/6P/xwDHAOgB6AEGAwYDIQQhBC0FLQUnBicGDQcNB9kH2QePCI8IOAk4CcwJzAlNCk0KwArAChsLGwtiC2ILnwufC8sLywvpC+kL/Qv9C/kL+QvgC+ALtwu3C3gLeAspCykL0ArQCmMKYwrlCeUJWAlYCbsIuwgTCBMIaQdpB7wGvAYQBhAGagVqBcgEyAQsBCwEmAOYAwwDDAOGAoYCBAIEAoMBgwECAQIBgACAAP7//v97/3v/+P74/nb+dv7z/fP9bf1t/en86fxk/GT84Pvg+2T7ZPvs+uz6ePp4+gr6Cvqe+Z75MPkw+cb4xvhb+Fv47Pfs9333ffcL9wv3kvaS9hz2HPas9az1PvU+9d703vSI9Ij0NfQ19O7z7vOy87Lzf/N/82HzYfNV81XzVfNV82fzZ/OI84jzsfOx8+/z7/NA9ED0nfSd9An1CfWB9YH1/fX99YX2hfYd9x33wvfC93j4ePg4+Tj5+vn6+bz6vPp9+337Ovw6/PX89fyp/an9T/5P/t/+3/5T/1P/pv+m/9f/1//n/+f/1v/W/6T/pP9R/1H/3f7d/k7+Tv6i/aL93vze/Af8B/wa+xr7FvoW+gT5BPnf99/3pvam9mf1Z/Ud9B30wfLB8mPxY/H87/zvhe6F7hntGe2167XrV+pX6hvpG+n75/vn8Obw5hTmFOZh5WHlz+TP5Hfkd+RR5FHkT+RP5IPkg+Tl5OXkauVq5SXmJeYR5xHnHuge6FjpWOm16rXqKuwq7MLtwu1+737vWvFa8VfzV/Nz9XP1ovei99753vkp/Cn8gv6C/uEA4QBHA0cDrQWtBf4H/gc7CjsKZgxmDHAOcA5iEGIQQxJDEvsT+xOQFZAVDRcNF1wYXBiQGZAZtxq3Gr0bvRupHKkchh2GHTseOx7SHtIeXh9eH8Yfxh8SIBIgTiBOIF4gXiBKIEogKSApIOgf6B+YH5gfTB9MH+we7B6AHoAeGR4ZHqQdpB0vHS8dzBzMHGQcZBz8G/wbnBucGy8bLxu8GrwaUhpSGt4Z3hlkGWQZ6hjqGFkYWRi3F7cXERcRF14WXhalFaUV8hTyFDQUNBRsE2wToxKjEswRzBHsEOwQChAKEBoPGg8ZDhkOCQ0JDeIL4gulCqUKWAlYCfkH+QeFBoUG/wT/BGcDZwO+Ab4BDQANAF7+Xv6z/LP8DvsO+3b5dvnn9+f3XPZc9uT05PR683rzGfIZ8s/wz/CT75PvXu5e7kPtQ+077DvsQetB62rqauqv6a/pBukG6Ybohugp6Cno5+fn59Tn1Ofq5+rnHegd6Hvoe+j+6P7omOmY6VfqV+o06zTrIuwi7CntKe1C7kLuYO9g74rwivC+8b7x8/Lz8i/0L/Ry9XL1s/az9vj3+Pc/+T/5hfqF+sj7yPsI/Qj9QP5A/mr/av+FAIUAjQGNAX8CfwJcA1wDJgQmBNgE2AR2BXYFBgYGBoMGgwb0BvQGYAdgB8EHwQccCBwIdgh2CMkIyQgZCRkJbgluCb8JvwkSChIKagpqCr0KvQoMCwwLXAtcC6MLowvmC+YLKgwqDGUMZQyaDJoM0gzSDAINAg0zDTMNbQ1tDacNpw3iDeINJA4kDl0OXQ6QDpAOxA7EDuoO6g4DDwMPEQ8RDwIPAg/YDtgOmg6aDj0OPQ7EDcQNNg02DYgMiAy8C7wL2QrZCt8J3wnVCNUIwwfDB6YGpgZ6BXoFQARABPgC+AKjAaMBRQBFAOT+5P59/X39DvwO/J36nfoq+Sr5uPe491b2VvYE9QT1u/O784ryivJt8W3xYPBg8Hnvee+z7rPuCO4I7oXthe0g7SDtz+zP7KPso+yW7Jbsn+yf7MnsyewL7QvtW+1b7cXtxe1H7kfu2u7a7obvhu9G8EbwDvEO8ebx5vHO8s7ywvPC88z0zPTn9ef1CPcI9y74LvhS+VL5cPpw+on7ifue/J78qv2q/ar+qv6c/5z/fgB+AFIBUgEcAhwC4ALgApsDmwNOBE4E+wT7BJwFnAU4BjgG0gbSBmUHZQfxB/EHdQh1COYI5ghFCUUJmAmYCdcJ1wkICggKLwovCkYKRgpPCk8KUQpRCkoKSgo+Cj4KNAo0CiMKIwoKCgoK7gnuCccJxwmXCZcJYwljCSQJJAnZCNkIggiCCBoIGgiiB6IHHwcfB5MGkwb9Bf0FYgViBb8EvwQWBBYEagNqA70CvQISAhICaQFpAcQAxAAfAB8AfP98/97+3v5D/kP+qf2p/RT9FP19/H384Pvg+0H7Qfuc+pz68vny+Uv5S/ml+KX4/vf+92H3YffM9sz2PfY99r/1v/VQ9VD16PTo9JD0kPRB9EH08fPx86vzq/Nq82rzKvMq8/by9vLM8szypPKk8ozyjPJ+8n7ydvJ28n3yffKR8pHyrfKt8t3y3fIg8yDzcvNy89zz3PNc9Fz06/Tr9I71jvVA9kD2+/b79sH3wfeR+JH4Zfll+UH6Qfol+yX7D/wP/P38/fzx/fH95v7m/tj/2P/IAMgAtAG0AZgCmAJ1A3UDSwRLBBEFEQXFBcUFYQZhBtsG2wYuBy4HWAdYB1MHUwceBx4HvQa9BjAGMAZ4BXgFngSeBKQDpAOLAosCUwFTAQEAAQCQ/pD+//z//Fv7W/uh+aH51PfU9wP2A/Yp9Cn0RPJE8mfwZ/CM7ozuruyu7OXq5eoq6Srpd+d35+fl5+V55HnkJuMm4w/iD+It4S3hceBx4PTf9N+t363fj9+P37Xftd8Y4BjgquCq4Hvhe+GC4oLiq+Or4wblBuWM5ozmLOgs6O7p7unL68vruO247b7vvu/k8eTxJfQl9IH2gfb1+PX4c/tz++797v1lAGUA1wLXAjcFNwWIB4gHyAnICeML4wvgDeANyA/ID4wRjBE4EzgT1RTVFEkWSRacF5wX2xjbGPUZ9Rn7GvsaABwAHOoc6hzEHcQdmB6YHk4fTh/zH/MfmyCbICghKCGjIaMhGSIZImwibCKsIqwi7SLtIhsjGyNCI0IjcyNzI44jjiOeI54jtCO0I7YjtiOuI64jryOvI5ojmiN3I3cjUiNSIw8jDyOyIrIiSiJKIrkhuSEFIQUhPCA8IEgfSB8xHjEeBx0HHbobuhtRGlEa3BjcGE0XTRenFacV8RPxEyESIRI1EDUQOQ45Di0MLQwRChEK6wfrB7wFvAV+A34DNQE1Aer+6v6e/J78VfpV+iD4IPj59fn13/Pf8+bx5vEM8AzwTu5O7sLswuxc61zrEeoR6vHo8ej15/XnFOcU52bmZubh5eHld+V35TnlOeUb5RvlEuUS5THlMeVw5XDlwOXA5TLmMua+5r7mWuda5xroGuj86Pzo8uny6QbrBusr7CvsUe1R7X3ufe6s76zv1fDV8P/x//Em8ybzQfRB9FT1VPVg9mD2Yfdh91/4X/ha+Vr5TvpO+jz7PPsm/Cb8C/0L/fD98P3Y/tj+wv/C/6sAqwCSAZIBdAJ0AkoDSgMYBBgE4ATgBKEFoQVgBmAGJgcmB+wH7Ae5CLkIkQmRCWsKawpKC0oLNQw1DBsNGw0DDgMO8A7wDtMP0w+xELEQkxGTEWYSZhIvEy8T9hP2E6MUoxQ9FT0VyBXIFTIWMhaAFoAWvRa9FtcW1xbXFtcWyRbJFpwWnBZUFlQW+BX4FXgVeBXVFNUUHBQcFEITQhNLEksSQBFAERQQFBDGDsYOXg1eDdkL2Qs8CjwKkAiQCNYG1gYMBQwFOAM4A18BXwGG/4b/sv2y/fD78Ps4+jj6hviG+OD24PZB9UH1p/On8ybyJvK58LnwXO9c7xzuHO7x7PHs0uvS69Xq1er26fbpK+kr6YnoiegM6Azoqueq53rneud653rnnOec5+3n7edc6Fzo1OjU6F/pX+n36ffpleqV6krrSusR7BHs3+zf7Lvtu+2i7qLui++L74LwgvCE8YTxh/KH8pDzkPOa9Jr0n/Wf9an2qfa997331fjV+PD58PkE+wT7BvwG/PP88/zR/dH9p/6n/n3/ff9YAFgANQE1AQkCCQLSAtICkAOQA0IEQgTxBPEEowWjBVAGUAb5BvkGoQehB0AIQAjeCN4IggmCCSEKIQq4CrgKQwtDC7MLswsNDA0MXQxdDKAMoAzeDN4MGg0aDUUNRQ1eDV4NbQ1tDWwNbA1hDWENVA1UDTQNNA39DP0MrgyuDD8MPwy2C7YLHAscC24KbgqsCawJ1wjXCOkH6QfjBuMG0AXQBbQEtASUA5QDdgJ2Al0BXQFHAEcAN/83/zL+Mv42/Tb9P/w//FH7Uftm+mb6ffl9+aP4o/jb99v3J/cn95P2k/Yb9hv2t/W39Wz1bPUz9TP1BfUF9er06vTf9N/04PTg9Pf09/Qh9SH1WvVa9ab1pvX/9f/1W/Zb9rv2u/YY9xj3avdq97f3t/f89/z3N/g3+G/4b/im+Kb41/jX+An5Cfk4+Tj5X/lf+YP5g/mh+aH5uPm4+c35zfnh+eH59fn1+RH6Efoz+jP6WfpZ+oT6hPqx+rH63frd+hH7EftT+1P7p/un+xL8EvyU/JT8Kv0q/dD90P2H/of+T/9P/yYAJgALAQsB+QH5AeQC5ALLA8sDsQSxBJAFkAVvBm8GTQdNBxsIGwjPCM8IZQllCdEJ0QkTChMKLgouChkKGQrOCc4JSwlLCY0IjQiWB5YHcAZwBiAFIAWjA6MD9AH0ARMAEwD4/fj9qfup+z35Pfm49rj2HfQd9IHxgfHa7truIuwi7HjpeOnZ5tnmQ+RD5Nzh3OGY35jfa91r3XjbeNu52bnZJNgk2OTW5Nbt1e3VKdUp1bbUttSK1IrUl9SX1AXVBdXU1dTV89bz1nXYddhK2kraUtxS3Jnemd4U4RThtOO044DmgOZ06XTpfux+7Jvvm+/L8svyBPYE9kH5QfmB/IH8tv+2/88CzwLMBcwFrgiuCGsLawsPDg8OoBCgEAUTBRNBFUEVWBdYFzEZMRndGt0abhxuHM0dzR0GHwYfKCAoIBYhFiHjIeMhpSKlIkgjSCPeI94jdyR3JPck9yRsJWwl6yXrJVsmWybMJswmUydTJ8wnzCdEKEQozCjMKEMpQym4KbgpPyo/Kq4qrioOKw4rbytvK6srqyvRK9Er+Sv5KwAsACzyK/Ir3iveK50rnSs1KzUrtSq1Kv0p/SkRKREpBCgEKLcmtyYwJTAlfiN+I44hjiFlH2UfFB0UHZAakBrbF9sXAxUDFQYSBhLoDugOuAu4C4EIgQhEBUQFBgIGAtH+0f6d+537aPho+Ef1R/U58jnyP+8/73bsduzU6dTpUOdQ5wjlCOXz4vPiCeEJ4Wvfa98O3g7e3tze3Pjb+NtQ21Db2NrY2rHasdrQ2tDaHdsd26nbqdtf3F/cKt0q3R7eHt413zXfXOBc4KXhpeEA4wDjWuRa5MHlweUw5zDnnOic6BLqEuqL64vr+Oz47GDuYO7C78LvFvEW8WXyZfKs86zz4vTi9An2CfYf9x/3Ifgh+Bb5FvkB+gH64vri+r/7v/uc/Jz8f/1//W3+bf5s/2z/fgB+AJwBnAHKAsoCCAQIBE8FTwWoBqgGFQgVCIwJjAkSCxILrAysDEoOSg7yD/IPqRGpEVgTWBMAFQAVqRapFjUYNRiuGa4ZHRsdG2ocahydHZ0dxh7GHsofyh+yILIgiSGJISsiKyKYIpgi1yLXIsgiyCJyInIi5iHmIRAhECH5H/kfsh6yHiwdLB1xG3EblhmWGZUXlRd1FXUVRRNFE/4Q/hCfDp8OMAwwDLAJsAkgByAHiQSJBPQB9AFf/1//0fzR/FX6Vfrq9+r3lvWW9W7zbvN08XTxpe+l7xHuEe6q7KrsXute6zrqOuo16TXpSehJ6JDnkOcE5wTnl+aX5lfmV+Y35jfmJeYl5jTmNOZa5lrmg+aD5rrmuubu5u7mEOcQ5zbnNudf51/ng+eD57fnt+fw5/DnHegd6FLoUuiP6I/o0ejR6DHpMemo6ajpJuom6rTqtOpF60Xrz+vP62HsYez77Pvsme2Z7UruSu4T7xPv8+/z7/vw+/At8i3yg/OD8/30/fSX9pf2SPhI+A76Dvrr++v72P3Y/c7/zv/LAcsBywPLA8MFwwW2B7YHpAmkCX4LfgtIDUgNBg8GD6YQphAtEi0SnhOeE98U3xTwFfAV1RbVFnsXexftF+0XOhg6GFAYUBgzGDMY7RftF28XbxfAFsAW7xXvFfEU8RTKE8oTgxKDEhIREhGBD4EP3w3fDTEMMQx/Cn8K0AjQCCcHJweGBYYF9wP3A4ECgQInAScB5v/m/7z+vP6i/aL9lfyV/Jz7nPu6+rr67vnu+T/5P/mm+Kb4IPgg+LT3tPdi92L3J/cn9wf3B/f59vn29Pb09vv2+/YM9wz3Ivci9z/3P/dZ91n3Zfdl92P3Y/dS91L3MPcw9wv3C/fj9uP2s/az9oP2g/ZL9kv2CfYJ9sX1xfV+9X71L/Uv9eT05PSc9Jz0VPRU9B30HfT28/bz3fPd89rz2vPp8+nzAvQC9Cz0LPRk9GT0pPSk9PX09fRX9Vf1yvXK9VX2Vfb69vr2tPe094j4iPh1+XX5e/p7+pn7mfvN/M38Df4N/k7/Tv+MAIwAxwHHAfsC+wIwBDAEYwVjBYkGiQacB5wHmwibCH8JfwlSClIKHAscC9EL0QttDG0M7QztDEQNRA16DXoNoQ2hDbQNtA24DbgNpw2nDW8Nbw0MDQwNigyKDOwL7As6CzoLdgp2CpMJkwmDCIMIQwdDB9cF1wVFBEUEmQKZAtwA3AAH/wf/Gv0a/R37HfsP+Q/58/bz9tv02/S48rjyf/B/8EHuQe756/nrrOms6YLngud85Xzlj+OP49Lh0uEw4DDgl96X3ijdKN3b29vbpdql2qvZq9nf2N/YMdgx2MHXwdeJ14nXeNd417LXstcr2CvY0djR2L7Zvtns2uzaSNxI3Obd5t2+377ft+G34dzj3OMk5iTmgeiB6Pvq++qU7ZTtQfBB8ATzBPPg9eD1zfjN+Mb7xvvM/sz+1QHVAc8EzwS9B70HnQqdCmINYg0ZEBkQyRLJElsVWxXbF9sXUxpTGqkcqRzwHvAeNyE3IVsjWyNkJWQlXSddJx4pHim5KrkqSixKLLItsi0HLwcvYTBhMJUxlTGyMrIy0jPSM8Q0xDSXNZc1XjZeNuc25zZAN0A3hTeFN483jzdxN3E3RjdGN+E24TZJNkk2mDWYNaY0pjSEM4QzTTJNMt8w3zBAL0AvhC2ELYoriitaKVopBycHJ3okeiS5Ibkh1R7VHsYbxhuWGJYYVhVWFQcSBxKlDqUOMwszC7MHswcjBCMEjACMAAH9Af2A+YD5DPYM9rryuvKD74PvZ+xn7IXphenR5tHmPuQ+5OLh4uGt363fkN2Q3a/br9sB2gHaddh12CrXKtcQ1hDWE9UT1VvUW9Th0+HTlNOU05XTldPP08/TItQi1KnUqdRX1VfVGtYa1hTXFNc42DjYbNls2cPaw9ox3DHcod2h3SvfK9/J4Mnga+Jr4iLkIuTp5enltOe055Ppk+mI64jrh+2H7Zfvl++y8bLx0PPQ8/L18vUa+Br4RPpE+nD8cPyh/qH+2QDZAAwDDANABUAFcQdxB48JjwmjC6MLtA20DbYPtg+3EbcRwBPAE7cVtxWfF58Xfxl/GT0bPRvoHOgckB6QHhcgFyCHIYch6SLpIhkkGSQiJSIlGCYYJtsm2yZ4J3gn/if+J0goSChhKGEoXiheKCMoIyi+J74nRCdEJ5Ymlia/Jb8l1CTUJMEjwSOQIpAiUyFTIfIf8h9tHm0ezxzPHAIbAhsLGQsZ9hb2Fr0UvRRkEmQS+A/4D3wNfA3xCvEKYAhgCM8FzwU4AzgDoQChABL+Ev6I+4j7BfkF+Zb2lvYx9DH00fHR8Ybvhu9G7UbtCOsI6+fo5+jc5tzm3eTd5ArjCuNY4Vjhud+530neSd763PrcvNu826/ar9rI2cjZ+dj52GTYZNj71/vXrNes15bXlteu167X4dfh10rYStjZ2NnYdtl22TnaOdoX2xfbAdwB3BLdEt1B3kHeft9+39rg2uBQ4lDi1uPW44LlguVS51LnN+k36TbrNutH7UftYe9h74zxjPHR89HzK/Yr9pT4lPgE+wT7av1q/bj/uP/0AfQBIgQiBD0GPQZPCE8IVgpWCkAMQAwVDhUO3A/cD4URhREZExkTmhSaFOsV6xURFxEXFBgUGOQY5BiNGY0ZHRodGnwafBqqGqoaqxqrGmkaaRruGe4ZThlOGYIYghiWF5YXlxaXFncVdxU5FDkU5BLkEnERcRHkD+QPSA5IDp8MnwztCu0KPQk9CZIHkgfuBe4FVARUBMYCxgJAAUABwf/B/03+Tf7l/OX8ivuK+0j6SPof+R/5DfgN+Bv3G/dF9kX2h/WH9ez07PRu9G70A/QD9LHzsfNr82vzKvMq8/by9vLL8svyovKi8oTyhPJm8mbyPfI98hTyFPLo8ejxu/G78Z7xnvGN8Y3xhPGE8YzxjPGj8aPxxvHG8QDyAPJM8kzynfKd8vXy9fJO807zpvOm8wz0DPSI9Ij0GfUZ9cH1wfV59nn2Nfc19/j3+PfD+MP4mPmY+Xz6fPpx+3H7c/xz/ID9gP2b/pv+wv/C/+8A7wAhAiECUwNTA3oEegSVBZUFpwanBqQHpAeMCIwIXQldCQgKCAqQCpAK/Ar8CkkLSQuCC4ILswuzC9QL1AvsC+wLAwwDDBIMEgwfDB8MMQwxDDsMOww8DDwMOQw5DCYMJgwIDAgM5gvmC7gLuAt9C30LOAs4C98K3wp5CnkKEgoSCqwJrAlRCVEJCwkLCdMI0wikCKQIfQh9CFEIUQgZCBkI0wfTB3UHdQf3BvcGVgZWBooFigWKBIoEVANUA+oB6gFHAEcAbP5s/mf8Z/w4+jj64vfi9331ffUA8wDzavBq8M/tz+0q6yrrd+h36N7l3uVc41zj7eDt4LHesd6X3JfcjNqM2rLYstj91v3WYNVg1QfUB9Tn0ufS79Hv0UnRSdHv0O/Q1NDU0CLRItHU0dTR1dLV0kLUQtQS1hLWL9gv2K3ardp83XzdeeB54J/jn+PZ5tnmEuoS6lLtUu2e8J7w8/Pz80/3T/ew+rD6Dv4O/l4BXgGqBKoE9Af0BykLKQtNDk0OXBFcETwUPBT9Fv0WrxmvGTscOxytHq0eCyELISsjKyMbJRsl8CbwJpMokygdKh0qpSulKwItAi0/Lj8udC90L30wfTB3MXcxgTKBMnYzdjNlNGU0ZTVlNUM2QzYONw432TfZN204bTjNOM04BzkHOec45zh2OHY40zfTN9Y21jaQNZA1HDQcNFoyWjJYMFgwMi4yLtIr0is+KT4phyaHJpYjliNpIGkgDx0PHX0ZfRm0FbQVwxHDEawNrA1yCXIJIwUjBdIA0gB//H/8Lvgu+Pjz+PPZ79nv0evR6wjoCOh75HvkKOEo4TbeNt6a25rbQ9lD2VbXVtfB1cHVbNRs1HbTdtPI0sjSR9JH0hDSENIT0hPSONI40p7SntI00zTT49Pj08nUydTe1d7VDtcO13XYddgI2gjar9uv23rdet1c31zfP+E/4S7jLuMe5R7l+Ob45sjoyOiK6orqNuw27Nzt3O1/73/vGvEa8bXytfJV9FX0+fX59aj3qPdt+W35SvtK+z79Pv1P/0//fgF+AcQDxAMnBicGpwinCCwLLAu5DbkNThBOENAS0BJMFUwVyxfLFzIaMhqOHI4c6R7pHiEhISFEI0QjYyVjJVgnWCctKS0p7iruKmwsbCyyLbItzy7PLp0vnS8pMCkwhzCHMI8wjzBJMEkwyC/IL+ou6i68LbwtVyxXLKUqpSq1KLUooCagJlckVyTmIeYhZB9kH8YcxhwTGhMaXRddF50UnRTXEdcRFg8WD1kMWQydCZ0J6QbpBjsEOwSNAY0B3/7f/jr8Ovya+Zr5A/cD94z0jPQ08jTy/O/87/zt/O0r7CvsfOp86vvo++iP54/nG+Yb5qzkrOQu4y7jlOGU4QPgA+B53nne7tzu3Ibbhtsz2jPa5Njk2L7Xvte41rjWx9XH1RLVEtWL1IvUGdQZ1NnT2dO9073TsNOw09nT2dMw1DDUo9Sj1FPVU9U31jfWQddB15DYkNge2h7a1dvV28Ldwt3Y39jfAOIA4kjkSOS05rTmO+k76enr6eu97r3uqPGo8af0p/S397f3zPrM+tb91v3OAM4ApAOkA0QGRAavCK8I6wrrCu8M7wzJDskOhhCGEBkSGRKRE5ET/hT+FE4WThaJF4kXshiyGKkZqRltGm0aBhsGG2AbYBuGG4YbiRuJG1UbVRvxGvEabBpsGroZuhntGO0YGhgaGDwXPBdbFlsWgxWDFaQUpBS9E70T1BLUEtcR1xHGEMYQpw+nD3MOcw4uDS4N4gviC4oKigoiCSIJrgeuByoGKgaWBJYE9wL3Ak8BTwGZ/5n/1f3V/Q/8D/xJ+kn6jviO+PD28PZv9W/1BPQE9LnyufKE8YTxXvBe8FbvVu9j7mPud+137ZrsmuzD68Pr7Ors6jTqNOqf6Z/pLuku6ffo9+j16PXoHOkc6X3pfekV6hXq1urW6sfrx+vZ7Nns9u327SDvIO9R8FHwfvF+8a7yrvLb89vzAfUB9Sf2J/ZV91X3j/iP+Nn52fkw+zD7ivyK/N793v0q/yr/cgByALUBtQH3AvcCNwQ3BGYFZgWFBoUGlgeWB5IIkgiBCYEJZApkCioLKgvTC9MLZAxkDNQM1AwxDTENhg2GDcwNzA0KDgoOSA5IDoAOgA6/Dr8OEA8QD2UPZQ+9D70PEhASEE0QTRBoEGgQahBqEEUQRRD/D/8PoA+gDyAPIA+CDoIO0A3QDQgNCA0yDDIMYQthC5gKmArmCeYJVwlXCeII4giBCIEIKwgrCM4HzgdhB2EH4gbiBk4GTganBacF8gTyBDQENARyA3IDtAK0Av0B/QFIAUgBkwCTANn/2f8c/xz/Yv5i/rb9tv0b/Rv9ivyK/P37/ftd+137lfqV+qH5oflz+HP4A/cD92D1YPWG84bzevF68V7vXu8w7TDt7Ors6qnoqehW5lbm6uPq45HhkeFU31TfOd053W/bb9vo2ejZidiJ2GzXbNd81nzWpdWl1Q3VDdWm1KbUWtRa1ErUStRx1HHUxtTG1HTVdNV61nrWwtfC117ZXtlB20HbW91b3cffx9+L4ovinOWc5fbo9uiJ7InsMfAx8N7z3vOG94b3Hvse+5r+mv74AfgBNAU0BTwIPAgcCxwL4A3gDXoQehD8EvwScRVxFcEXwRf6GfoZKhwqHDgeOB4xIDEgISIhIuUj5SOIJYglGCcYJ3QodCiqKaopzSrNKrUrtStwLHAsFi0WLYgtiC3iLeItRC5ELpEukS7bLtsuOS85L38vfy+5L7kv+i/6LxgwGDAZMBkwDDAMMMIvwi88Lzwvii6KLoYthi03LDcsryqvKtAo0CihJqEmOCQ4JIkhiSGkHqQephumG5AYkBhkFWQVLxIvEuoO6g6RC5ELLAgsCMQExARZAVkB8P3w/ZX6lfpA90D37vPu87bwtvCS7ZLtgeqB6qPno+fy5PLkbeJt4jjgOOBR3lHesdyx3HjbeNuZ2pna/9n/2cHZwdnP2c/ZD9oP2pbaltpP20/bH9wf3BXdFd0k3iTeN98332XgZeCm4abh7OLs4knkSeS55bnlMucy58ToxOhv6m/qK+wr7ATuBO757/nv//H/8Rb0FvQ39jf2VPhU+GX6Zfpq/Gr8Xv5e/j8APwATAhMC3gPeA5oFmgVRB1EHDAkMCcUKxQqIDIgMXA5cDi8QLxAKEgoS9RP1E9oV2hXAF8AXrBmsGXcbdxsgHSAdpx6nHuwf7B/3IPcg1yHXIXIiciLQItAiAyMDI/Mi8yKvIq8iSyJLIrQhtCHxIPEgFCAUIAkfCR/bHdsdoBygHEwbTBvlGeUZcxhzGOQW5BY3FTcVdxN3E58RnxGzD7MPvQ29DbwLvAuwCbAJowejB5sFmwWfA58DtwG3Aez/7P87/jv+pvym/DP7M/vc+dz5m/ib+HH3cfdO9k72JPUk9frz+vPF8sXyfvF+8THwMfDV7tXuYO1g7efr5+to6mjq4Ojg6G7nbucK5grmqOSo5GHjYeMo4iji8eDx4Nvf29/f3t/e7d3t3SfdJ91/3H/c59vn23zbfNsx2zHb99r32una6doE2wTbPts+27zbvNt/3H/cfN183cneyd5c4FzgG+Ib4hLkEuQ55jnmfuh+6Onq6ep07XTtD/AP8LXytfJf9V/1APgA+I76jvoK/Qr9cP9w/7sBuwHyA/IDFAYUBhcIFwj8CfwJxAvEC2ANYA3UDtQOKRApEE0RTRFHEkcSHBMcE7oTuhMmFCYUbBRsFIAUgBRvFG8USRRJFAYUBhSzE7MTXxNfE/wS/BKOEo4SHBIcEpgRmBENEQ0RjRCNEBcQFxC0D7QPag9qDyMPIw/TDtMOcw5zDvAN8A1NDU0NlwyXDM0LzQv5CvkKJAokCkkJSQlhCGEIaAdoB1UGVQYjBSMF1wPXA3wCfAIaARoBuv+6/2X+Zf4P/Q/9rfut+z76Pvq3+Lf4GPcY93X1dfXU89TzO/I78sTwxPBv72/vOe457jLtMu1S7FLsjuuO6/jq+OqM6ozqRepF6jfqN+pe6l7qqeqp6iLrIuu9673rauxq7DPtM+0V7hXuA+8D7wXwBfAW8RbxK/Ir8kzzTPN39Hf0qPWo9eT25PYq+Cr4dfl1+cP6w/oS/BL8Wf1Z/ZD+kP61/7X/xQDFAMMBwwG3ArcCqgOqA5gEmASDBYMFZgZmBjgHOAf+B/4HwgjCCIIJgglKCkoKIgsiC/4L/gvfDN8MyA3IDacOpw56D3oPQRBBEOgQ6BByEXIR5xHnETwSPBJ3EncSnhKeEp8SnxJ5EnkSMxIzEsARwBEtES0RiBCIEMwPzA//Dv8OKg4qDkgNSA1cDFwMbQttC3MKcwppCWkJTAhMCBUHFQfBBcEFWwRbBOsC6wJ7AXsBFQAVAMP+w/6J/Yn9a/xr/HP7c/uj+qP6/vn++Yr5ivlI+Uj5Nfk1+VX5Vfmh+aH5DPoM+or6ivoI+wj7c/tz+8H7wfvw+/D7BfwF/A78DvwW/Bb8Hfwd/CL8IvwX/Bf88Pvw+6v7q/tI+0j7zvrO+k36Tfq++b75FfkV+Ur4SvhC90L36/Xr9VD0UPRv8m/yUfBR8B3uHe7f69/roOmg6YbnhueM5Yzlp+On4/Ph8+Fo4GjgBt8G3/vd+91N3U3d9dz13AXdBd1k3WTd6d3p3Z/en95333ffZeBl4IThhOHQ4tDiOeQ55MrlyuWA54DnTOlM6TvrO+tH7UftYu9i74/xj/HU89TzMPYw9qj4qPg5+zn70P3Q/U0ATQCiAqICxwTHBLoGugaMCIwISQpJCuQL5AtbDVsNsw6zDuAP4A/+EP4QJxInElYTVhOeFJ4UCBYIFn0XfRcMGQwZwRrBGn4cfhxGHkYeGCAYIMshyyFnI2cjACUAJXImcibJJ8knCikKKQkqCSrRKtEqeyt7K/Ar8CtELEQsjyyPLKgsqCySLJIsXixeLOsr6ytCK0IrcipyKlQpVCnoJ+gnPiY+JkMkQyQEIgQilx+XH+8c7xwJGgka+Bb4Fr4TvhNwEHAQLg0uDQUKBQr6BvoGDgQOBEcBRwGk/qT+LPws/PD58Pnq9+r3EPYQ9mT0ZPTZ8tnyZfFl8RbwFvDj7uPuwe3B7b3svezP68/r8ury6j/qP+qx6bHpPOk86e3o7ei46LjokeiR6I3ojeil6KXozejN6A7pDulX6VfpkOmQ6cnpyen76fvpIOog6k7qTuqE6oTquuq66gfrB+tq62rr4Ovg63nseew27TbtEO4Q7hXvFe9M8EzwsfGx8UvzS/MU9RT1/fb99vn4+fgC+wL7EP0Q/Rv/G/8pASkBNQM1AzQFNAUpBykHEwkTCecK5wqtDK0Mag5qDg4QDhCfEZ8RJRMlE4sUixTWFdYVCxcLFw4YDhjhGOEYihmKGfIZ8hkgGiAaJBokGu4Z7hmKGYoZCBkIGV0YXRiRF5EXtha2FsQVxBXFFMUUyhPKE8sSyxLPEc8R3xDfEPEP8Q8JDwkPKw4rDk0NTQ1sDGwMiguKC5oKmgqbCZsJkQiRCHwHfAddBl0GOwU7BRMEEwTjAuMCqQGpAWgAaAAe/x7/zv3O/X78fvwp+yn7zfnN+XT4dPgV9xX3rPWs9UX0RfTW8tbyV/FX8dzv3O9l7mXu9ez17Kvrq+uE6oTqeOl46Zfol+jQ59DnE+cT53TmdObu5e7lfeV95T7lPuUt5S3lP+U/5YDlgOXi5eLlUuZS5tzm3OZ853znK+gr6Pvo++jp6enp5erl6vbr9usT7RPtLe4t7lDvUO958HnwofGh8c/yz/IC9AL0NfU19W32bfar96v35fjl+Bj6GPo/+z/7Wvxa/G39bf2E/oT+pv+m/9AA0AD9Af0BJgMmAz0EPQRIBUgFUwZTBloHWgdkCGQIawlrCVsKWwouCy4L5AvkC3QMdAzoDOgMSQ1JDZENkQ3GDcYN9w33DR4OHg5HDkcOew57DqwOrA7YDtgOAQ8BDxcPFw8ZDxkPCw8LD90O3Q6KDooOFA4UDnENcQ2mDKYMvwu/C78KvwqwCbAJngieCI8HjweKBooGlQWVBa8ErwTSA9ID+gL6AiICIgJHAUcBaQBpAIr/iv+k/qT+s/2z/br8uvyz+7P7n/qf+or5ivlx+HH4UfdR9zb2NvYd9R31A/QD9Pzy/PIH8gfyIvEi8V3wXfCy77LvGe8Z76Luou5C7kLu7+3v7bLtsu2C7YLtVe1V7T7tPu077TvtRO1E7WztbO2v7a/tCe4J7oruiu407zTvA/AD8ADxAPEp8inydfN18+X05fRy9nL2D/gP+LL5svlR+1H75Pzk/Gj+aP7g/+D/TgFOAawCrAL3A/cDLwUvBU0GTQZcB1wHaQhpCHMJcwl+Cn4KiguKC4AMgAxfDV8NKQ4pDtEO0Q5dD10P0A/QDxsQGxA+ED4QQhBCEB8QHxDdD90Pig+KDxoPGg+QDpAO8w3zDUANQA2EDIQMzQvNCxsLGwtzCnMK1wnXCT4JPgmnCKcIFwgXCIsHiwcGBwYHiwaLBhEGEQaXBZcFGAUYBY4EjgT3A/cDUwNTA6MCowLqAeoBLgEuAXcAdwDI/8j/J/8n/5n+mf4g/iD+vP28/XH9cf0+/T79Iv0i/Rz9HP0n/Sf9N/03/UT9RP0//T/9Hv0e/eD84PyF/IX8FfwV/KL7ovs1+zX71frV+of6h/pJ+kn6FPoU+ur56vnD+cP5nPmc+Xv5e/lf+V/5RPlE+Sz5LPkC+QL5rviu+CP4I/hN9033JvYm9sz0zPRQ81Dzx/HH8VfwV/D/7v/uuO247ZLskuyD64PrhOqE6rDpsOkK6QrpkeiR6F7oXuhp6GnonOic6Pjo+Ohl6WXpyunK6TPqM+qj6qPqGesZ67DrsOtr7GvsQu1C7T7uPu5W71bvgPCA8L/xv/ER8xHzcvRy9O317fWJ94n3RflF+Rr7Gvv5/Pn8yP7I/nIAcgDwAfABRwNHA4AEgASqBaoF0AbQBukH6Qf+CP4IGQoZCjoLOgtyDHIM0A3QDUEPQQ/JEMkQbBJsEhQUFBTMFcwVnBecF2IZYhkeGx4b0hzSHF0eXR7IH8gfHiEeITsiOyIlIyUj6SPpI24kbiTLJMskISUhJVklWSV5JXkliyWLJWklaSUXJRclrCSsJAwkDCQ8IzwjQSJBIvog+iBmH2YfmR2ZHYsbixtGGUYZ2hbaFkEUQRSEEYQRuw67DvcL9wtJCUkJvQa9BlMEUwQAAgACwv/C/6T9pP2p+6n71PnU+S74Lvio9qj2NfU19dzz3POV8pXyWvFa8TrwOvAp7ynvH+4f7jDtMO1b7Fvsn+uf6xHrEeum6qbqT+pP6hvqG+oJ6gnqFuoW6lfqV+rC6sLqPus+68rryutW7Fbs1+zX7F7tXu3t7e3tge6B7ibvJu/c79zvoPCg8IHxgfGD8oPynvOe89T01PQl9iX2jPeM9xD5EPm0+rT6c/xz/Dz+PP4FAAUAvwG/AV8DXwPpBOkEYAZgBr0HvQcBCQEJLQotCjULNQskDCQMBA0EDc4Nzg2JDokOPw8/D+IP4g+BEIEQKBEoEcsRyxFqEmoSBBMEE38TfxPcE9wTIhQiFEUURRROFE4URRRFFBoUGhTVE9UThROFEyUTJRPCEsISaxJrEhASEBKwEbARUBFQEeEQ4RBmEGYQ5Q/lD1IPUg+rDqsO8Q3xDRsNGw0oDCgMHQsdC/IJ8gmkCKQINQc1B6gFqAX+A/4DQQJBAngAeACm/qb+zfzN/Pn6+for+Sv5ZPdk97T1tPUV9BX0gvKC8gvxC/Gs76zvYe5h7kDtQO1C7ELsWuta65fql+rs6ezpS+lL6cfox+hc6FzoAugC6M3nzee357fntee159Xn1ecQ6BDoWOhY6MHowehM6Uzp8Onw6bzqvOqk66Trj+yP7H3tfe1h7mHuNO807wfwB/De8N7wsvGy8YjyiPJY81jzGvQa9Nj02PSa9Zr1ZfZl9j/3P/cl+CX4DvkO+fv5+/ny+vL6+Pv4+xD9EP06/jr+bv9u/50AnQDFAcUB5QLlAv0D/QMQBRAFHwYfBhsHGwcECAQI2wjbCJsJmwlLCksK8gryCoILggv7C/sLXQxdDKAMoAzSDNIMBg0GDUANQA2ODY4N9w33DWgOaA7ZDtkOQw9DD40PjQ+zD7MPuQ+5D5UPlQ9KD0oP4A7gDkwOTA6PDY8NswyzDLsLuwu1CrUKsQmxCbAIsAitB60HpAakBokFiQVVBFUECQMJA6wBrAE+AD4Awv7C/jz9PP2j+6P79fn1+T74Pvh79nv2svSy9Pby9vJI8Ujxqu+q7zbuNu7w7PDs2uva6wrrCut+6n7qKOoo6hDqEOop6inqYOpg6sDqwOo+6z7rxuvG62DsYOz/7P/smO2Y7TzuPO7w7vDute+175rwmvCd8Z3xr/Kv8tDz0PP59Pn0I/Yj9lL3UveE+IT4sfmx+dX61frp++n76fzp/NH90f2h/qH+WP9Y//T/9P91AHUA4QDhADwBPAGTAZMB8QHxAVwCXALcAtwCdgN2AycEJwTzBPME3QXdBdYG1gbdB90H6wjrCO4J7gnlCuUK0QvRC6QMpAxeDV4NBA4EDooOig76DvoOYA9gD7QPtA/+D/4PRRBFEHoQehCiEKIQxxDHEN0Q3RDpEOkQ7xDvENoQ2hCnEKcQVRBVENkP2Q8xDzEPZQ5lDm0NbQ1NDE0MEAsQC7oJuglVCFUI7AbsBoIFggUWBBYErgKuAlQBVAEVABUABP8E/y3+Lf6Q/ZD9H/0f/cn8yfx2/Hb8F/wX/Kv7q/s1+zX7vfq9+k36Tfro+ej5iPmI+S/5L/nd+N34lPiU+Gj4aPhk+GT4kPiQ+PL48vh9+X35Gvoa+rf6t/pH+0f7w/vD+zH8MfyW/Jb87/zv/Dn9Of1k/WT9X/1f/SH9If2d/J38yvvK+7D6sPpT+VP5wvfC9yX2JfaU9JT0HvMe89bx1vGq8Krwg++D72fuZ+5S7VLtSOxI7GzrbOu96r3qKuoq6rXptelH6Ufp0OjQ6GfoZ+gU6BTo3efd59/n3+cd6B3oiuiK6DTpNOkN6g3q/+r/6gzsDOws7SztWu5a7qnvqe8h8SHxvvK+8nz0fPRK9kr2DvgO+Lz5vPlS+1L7z/zP/Dj+OP6X/5f/8wDzAFACUAK7A7sDQQVBBdsG2waLCIsITgpOCg8MDwzRDdENoA+gD2wRbBE9Ez0TERURFcUWxRZVGFUYyBnIGQsbCxsxHDEcTh1OHUseSx4rHysf9h/2H48gjyAAIQAhXiFeIZIhkiGkIaQhoiGiIW4hbiEOIQ4hkSCRIOMf4x8LHwsfGx4bHv8c/xy9G70bYxpjGuEY4Rg3FzcXcxVzFY8TjxOYEZgRpg+mD74Nvg3sC+wLOgo6CqIIoggiByIHvgW+BXQEdARBA0EDJAIkAhgBGAERABEACf8J/wH+Af73/Pf86/vr++X65frh+eH53Pjc+OD34Pfs9uz2//X/9ST1JPVX9Ff0jvOO89by1vIs8izyjvGO8Q/xD/Gs8KzwXvBe8CzwLPAJ8Anw6e/p79Pv0+/E78Tvue+578Pvw+/o7+jvJvAm8I/wj/Al8SXx3/Hf8cPyw/LP88/z+fT59ET2RPaw97D3NPk0+cn6yfpp/Gn8Bv4G/pX/lf8UARQBgwKDAtwD3AMlBSUFXwZfBoIHggeRCJEIjwmPCXMKcwpFC0ULDQwNDMIMwgxuDW4NGw4bDrwOvA5UD1QP5w/nD18QXxC7ELsQ/hD+EBwRHBEZERkRAhECEc0QzRCAEIAQKBAoEL0PvQ9ND00P5g7mDoIOgg4jDiMOzA3MDWwNbA0ADQANjQyNDAsMCwx/C38L7wrvClIKUgqhCaEJ2QjZCPEH8QfqBuoGzQXNBaEEoQRwA3ADRQJFAigBKAEYABgAFf8V/xz+HP4n/Sf9M/wz/Ej7SPtm+mb6j/mP+cr4yvgN+A34TfdN94v2i/a+9b714fTh9Af0B/Qw8zDzWvJa8pbxlvHY8NjwFfAV8FnvWe+f7p/u5O3k7UPtQ+2/7L/sV+xX7BrsGuwA7ADs9ev16wPsA+wb7BvsMewx7FPsU+x87Hzso+yj7Njs2Owb7RvtaO1o7dXt1e1j7mPuDu8O797v3u/O8M7w0fHR8ezy7PIg9CD0ZfVl9bz2vPYh+CH4hfmF+eT65Po7/Dv8iv2K/c/+z/4LAAsAOAE4AUgCSAIyAzID9QP1A44EjgQNBQ0FgAWABe0F7QVhBmEG5QblBnYHdgccCBwI2QjZCJwJnAleCl4KFgsWC60LrQshDCEMdwx3DKcMpwy0DLQMpQylDHEMcQwdDB0Mtgu2Cz0LPQu6CroKMgoyCpkJmQnnCOcIGwgbCDEHMQctBi0GGQUZBfsD+wPaAtoCvgG+AbIAsgC6/7r/2P7Y/gn+Cf49/T39Zvxm/IX7hfuZ+pn6rfmt+dT41PgS+BL4Yvdi98j2yPY+9j72vfW99VL1UvX69Pr0rfSt9Gz0bPQs9Cz04vPi85nzmfNR81HzCPMI88vyy/KX8pfyZ/Jn8kvyS/JF8kXyT/JP8nHycfKf8p/yyPLI8vDy8PIU8xTzNPM082PzY/Ol86Xz+vP68230bfT59Pn0lvWW9Uj2SPYI9wj3z/fP9534nfhz+XP5UfpR+j37Pfs7/Dv8Tv1O/XP+c/6u/67/+wD7AFECUQKtA60DCgUKBVoGWgafB58H3AjcCAYKBgohCyELKgwqDBANEA3WDdYNhA6EDhIPEg+MD4wP/A/8D08QTxCEEIQQnhCeEJEQkRBmEGYQLRAtEN8P3w+ED4QPJg8mD78Ovw5TDlMO6A3oDW0NbQ3TDNMMFAwUDCQLJAsOCg4K7wjvCNoH2gfhBuEGBgYGBjsFOwVwBHAEowOjA9oC2gIrAisCqgGqAV4BXgE8ATwBKwErAQ4BDgHRANEAdQB1AAsACwCt/63/cf9x/2H/Yf91/3X/n/+f/8z/zP/u/+7/AQABAAMAAwD8//z/9P/0//L/8v8BAAEAJAAkAFgAWACQAJAAtgC2ALoAugCTAJMASwBLAPP/8/+W/5b/Mv8y/7f+t/78/fz94vzi/Gb7ZvuO+Y75dPd090z1TPUq8yrzFvEW8STvJO9D7UPtZutm657pnunr5+vnUuZS5gDlAOUB5AHkV+NX4xbjFuMj4yPjUuNS45zjnOPs4+zjOOQ45KbkpuQ/5T/l/uX+5ezm7Obx5/Hn8Ojw6PDp8Onw6vDq7+vv6wPtA+0r7ivuXe9d75/wn/Dt8e3xOvM684b0hvTK9cr1/Pb89iL4IvhI+Uj5ffp9+s37zftC/UL90f7R/mkAaQADAgMCoAOgAz8FPwX5BvkG3QjdCN8K3woBDQENOQ85D2YRZhGHE4cTphWmFawXrBeoGagZoRuhG3QddB0hHyEfqyCrIPEh8SH/Iv8i6iPqI5skmyQgJSAljSWNJcElwSXDJcMlpCWkJUclRyW3JLckCCQIJCQjJCMaIhoiACEAIcYfxh92HnYeIx0jHbsbuxtGGkYa1RjVGFwXXBfgFeAVbhRuFPcS9xJ6EXoR/g/+D30OfQ76DPoMgAuACw0KDQqjCKMIQgdCB+QF5AWABIAEEwMTA5oBmgEQABAAe/57/un86fxe+1773vne+XT4dPgZ9xn3xfXF9YP0g/RO807zI/Ij8hXxFfEg8CDwP+8/74Huge7j7ePtXO1c7f7s/uzG7MbsrOys7MDswOwB7QHtZe1l7fft9+2v7q/ufe9972PwY/Bb8VvxWfJZ8mPzY/N09HT0hPWE9ZP2k/ad9533nfid+Jf5l/mK+or6cvty+0/8T/wg/SD95/3n/an+qf5t/23/NwA3AAMBAwHUAdQBqwKrAoYDhgNsBGwEYAVgBVMGUwY/Bz8HHAgcCOAI4AiUCZQJSApICvsK+wq4C7gLgQyBDEQNRA3/Df8Ntg62DlwPXA/1D/UPhhCGEP4Q/hBeEV4RqBGoEc8RzxHWEdYRwhHCEYYRhhEmESYRrRCtEBYQFhBqD2oPtA60DuwN7A0RDRENKAwoDCwLLAskCiQKHgkeCR8IHwgyBzIHXwZfBqQFpAX9BP0EYgRiBMIDwgMNAw0DOQI5AkcBRwE+AD4AK/8r/x7+Hv4V/RX9CfwJ/PT69PrG+cb5fPh8+Cr3Kvfa9dr1l/SX9HLzcvNh8mHyUfFR8UnwSfA97z3vKO4o7iXtJe007DTsT+tP64rqiura6drpNek16bDosOhE6ETo5+fn56rnqueD54PnZudm523nbeeb55vn8ufy54joiOhU6VTpPOo86jvrO+s57DnsIO0g7f3t/e3S7tLuoO+g73bwdvBO8U7xGPIY8tby1vKF84XzKfQp9Nz03PSx9bH1sPaw9t333fcu+S75j/qP+vH78ftM/Uz9l/6X/s3/zf/wAPAA/wH/AfoC+gLuA+4D5QTlBOIF4gXoBugG9Af0B/EI8QjZCdkJsAqwCnALcAslDCUM2gzaDIINgg0bDhsOog6iDgcPBw9KD0oPdA90D3oPeg9iD2IPMw8zD+YO5g6ADoAOCg4KDn4Nfg3iDOIMQgxCDJwLnAv3CvcKWQpZCrcJtwkJCQkJSQhJCG0HbQd2BnYGaAVoBUgESAQXAxcD2gHaAZYAlgBT/1P/Gf4Z/vX89fzl++X73/rf+uT55Pno+Oj46Pfo9/L28vYG9gb2IfUh9Uv0S/R883zzrvKu8vHx8fFJ8UnxtPC08ELwQvDq7+rvou+i73Xvde9i72Lvae9p75rvmu/07/TvcPBw8BXxFfHe8d7xvvK+8rbztvO79Lv0u/W79bT2tPai96L3h/iH+G75bvlg+mD6X/tf+2r8avyB/YH9mv6a/rD/sP++AL4AvQG9AaQCpAJ0A3QDLgQuBNME0wRpBWkF+AX4BXkGeQbwBvAGYAdgB8UHxQcmCCYIjAiMCPAI8AhVCVUJwAnACSoKKgqXCpcKDgsOC4ALgAvoC+gLQAxADHYMdgyPDI8MmQyZDJUMlQyRDJEMlAyUDI8Mjwx6DHoMVQxVDBUMFQzFC8ULdgt2CywLLAvvCu8KxArECpsKmwpoCmgKJQolCsMJwwlGCUYJvQi9CC4ILgimB6YHKwcrB7cGtwZBBkEGxQXFBT0FPQWrBKsEFwQXBIcDhwMDAwMDkgKSAjgCOAL0AfQBwAHAAY4BjgFOAU4B9AD0AH0AfQDq/+r/QP9A/4b+hv6u/a79pvym/GH7YfvN+c356Pfo98v1y/WC84LzHPEc8b7uvu5u7G7sLuou6h7oHug65jrme+R75ADjAOPI4cjhz+DP4DjgOOD23/bf6d/p3wrgCuAw4DDgOuA64ELgQuBV4FXggOCA4Ovg6+CO4Y7hTuJO4i7jLuMh5CHkGOUY5SnmKeZR51Hnfeh96LLpsunn6ufqFOwU7E3tTe2V7pXu5O/k7zzxPPGY8pjy9vP282b1ZvX59vn2sviy+Iz6jPp//H/8ev56/nMAcwB7AnsCoQShBOkG6QZfCV8JAgwCDK8Orw5fEV8RCBQIFIQWhBbWGNYYBBsEG/Uc9Ry7HrseaiBqIOwh7CFRI1EjqiSqJNYl1iXkJuQm6CfoJ8EowSh3KXcpFSoVKm4qbiqEKoQqaSppKgQqBCpqKWopvii+KOwn7CcGJwYnJiYmJjIlMiU0JDQkOyM7IyoiKiIGIQYh3R/dH50enR5SHVIdExwTHM8azxqMGYwZThhOGP0W/RaSFZIVDhQOFGASYBKEEIQQgw6DDl4MXgwdCh0K0AfQB4QFhAVBA0EDEwETAQb/Bv8a/Rr9TvtO+6X5pfkP+A/4fvZ+9vX09fRp82nz2fHZ8VvwW/Dw7vDume2Z7WzsbOxm62brgeqB6tLp0ulR6VHp8ejx6LzovOin6Kfoouii6MLowugD6QPpXOlc6d3p3el76nvqI+sj69zr3Oud7J3sVe1V7Q7uDu667rruTO9M787vzu9D8EPwsPCw8C/xL/HI8cjxfPJ88lPzU/NG9Eb0SvVK9V/2X/aE94T3s/iz+Oz57Pku+y77dPx0/Lv9u/0I/wj/XABcALMBswESAxIDeQR5BNsF2wU6BzoHlwiXCOYJ5gkrCysLawxrDJcNlw2yDrIOwQ/BD7MQsxCNEY0RWBJYEgUTBROYE5gTGBQYFHIUchSqFKoUzBTMFM4UzhS6FLoUoRShFHcUdxREFEQUERQRFNAT0BOCE4ITMRMxE8wSzBJUElQSzxHPES0RLRFwEHAQnQ+dD6oOqg6YDZgNcAxwDC8LLwvbCdsJfgh+CBoHGgerBasFLgQuBKMCowIEAQQBU/9T/5z9nP3e+977F/oX+k74Tvh89nz2nfSd9MbyxvL68PrwOe8575vtm+0W7Bbsn+qf6kzpTOkY6Bjo/+b/5hzmHOZi5WLlveS95DrkOuTF48XjUeNR4/fi9+K04rTifeJ94mfiZ+Jn4mfibOJs4pDikOLS4tLiLeMt47fjt+Nl5GXkIeUh5fbl9uXd5t3mzefN593o3egL6gvqR+tH65bsluzt7e3tQO9A75vwm/AD8gPydfN18/f09/SF9oX2GfgZ+LX5tflf+1/7Gf0Z/eH+4f60ALQAjAKMAloEWgQfBh8G3QfdB4gJiAkjCyMLrQytDBAOEA5PD08PcRBxEGgRaBFBEkESBxMHE6oTqhM0FDQUsBSwFBAVEBVbFVsVmRWZFbQVtBWwFbAVlxWXFVwVXBUJFQkVqBSoFC4ULhScE5wT/BL8EkMSQxJ3EXcRohCiELsPuw/EDsQOxQ3FDbcMtwygC6ALhwqHCmkJaQlECEQIGgcaB+kF6QWuBK4EbANsAyUCJQLUANQAev96/xr+Gv61/LX8T/tP+/P58/mg+KD4VPdU9xv2G/bx9PH01vPW89ny2fL58fnxLfEt8YDwgPDr7+vvZO9k7/zu/O6t7q3ub+5v7kzuTO477jvuMO4w7jvuO+5Z7lnug+6D7sXuxe4a7xrvc+9z79zv3O9P8E/wyPDI8FLxUvHu8e7xl/KX8lPzU/Mg9CD09fT19NP10/W49rj2oPeg95H4kfiN+Y35lPqU+qf7p/vD/MP84/3j/QH/Af8bABsAMQExATwCPAI/Az8DOwQ7BCoFKgUQBhAG8AbwBsEHwQeGCIYIPgk+Cd8J3wlsCmwK7ArsClgLWAu3C7cLDwwPDFQMVAyIDIgMsgyyDMgMyAzODM4MzQzNDLsMuwydDJ0MfQx9DFYMVgwwDDAMFgwWDP4L/gvoC+gL0wvTC7QLtAuKC4oLXgteCykLKQvxCvEKuAq4CnkKeQo0CjQK8wnzCbAJsAlvCW8JMQkxCegI6AiOCI4IIwgjCKUHpQcWBxYHfgZ+BtQF1AUKBQoFEgQSBOIC4gJzAXMBx//H/+r96v3c+9z7m/mb+Tv3O/fE9MT0TfJN8gXwBfD87fztNew17LzqvOp96X3pYOhg6Hfnd+e85rzmIeYh5rLlsuVS5VLl5OTk5HbkduQM5Azkq+Or437jfuOC44LjouOi4+bj5uM75DvkkeSR5P/k/+SC5YLlCeYJ5p/mn+Y55znn0OfQ54Pog+hd6V3pV+pX6nbrduum7Kbsyu3K7ebu5u7/7//vGfEZ8UnySfKX85fzAvUC9Y/2j/ZK+Er4Ofo5+l38Xfy4/rj+PQE9Ac4DzgNfBl8G4wjjCEILQguADYANog+iD5oRmhF4E3gTTxVPFRMXExfWGNYYpBqkGlocWhz+Hf4dlh+WHwEhASFHIkcieCN4I3IkciRCJUIl/SX9JYgmiCb3JvcmYCdgJ6EnoSfBJ8EnyyfLJ54nnidIJ0gn4ybjJlomWibCJcIlMyUzJZUklST3I/cjbSNtI90i3SJLIksiuyG7IQchByEpICkgJB8kH+Id4h1qHGoc0xrTGhUZFRlAF0AXaBVoFYcThxOjEaMRxg/GD+oN6g0RDBEMPwo/CnAIcAihBqEG0gTSBAUDBQM5ATkBc/9z/7r9uv0L/Av8Y/pj+sn4yfg39zf3rvWu9UD0QPTu8u7ytPG08aHwofCo76jvv+6/7vXt9e1H7Uftreyt7DjsOOzb69vrguuC6zbrNuvo6ujqiuqK6jHqMerY6djpd+l36SbpJune6N7omuia6G/ob+hc6FzoWOhY6Hnoeei26LboAekB6WjpaOnh6eHpYeph6vnq+eqo66jrZ+xn7EPtQ+037jfuO+8771bwVvCI8YjxzfLN8iv0K/Sh9aH1Jfcl97T4tPhH+kf61/vX+179Xv3c/tz+TgBOAK8BrwEDAwMDTARMBIMFgwWrBqsGwwfDB78IvwiiCaIJcgpyCisLKwvWC9YLfAx8DBUNFQ2lDaUNMw4zDrIOsg4oDygPnA+cD/4P/g9REFEQmxCbENEQ0RD9EP0QKBEoEUYRRhFXEVcRXRFdEUQRRBERERERzRDNEHEQcRAEEAQQjA+MD/sO+w5MDkwOgg2CDZUMlQyKC4oLbApsCjwJPAn7B/sHrwavBlYFVgXuA+4DfAJ8AggBCAGS/5L/H/4f/rn8ufxd+137CvoK+s34zfim96b3lPaU9qP1o/XM9Mz0B/QH9F3zXfPG8sbyPPI88snxyfFj8WPx/fD98J/wn/BB8EHw3O/c74Xvhe887zzv/e797tXu1e697r3uqu6q7qjuqO607rTuyO7I7vLu8u4s7yzvb+9v78bvxu8t8C3wnPCc8BzxHPGn8afxNfI18s3yzfJs82zzDfQN9Lj0uPRs9Wz1JfYl9un26fa597n3kviS+Hb5dvli+mL6UPtQ+z38Pfwm/Sb9B/4H/t/+3/6v/6//dAB0ACwBLAHZAdkBfAJ8AhUDFQOoA6gDNAQ0BLcEtwQwBTAFowWjBQwGDAZwBnAG0QbRBioHKgd7B3sHxwfHBwgICAg9CD0IawhrCIcIhwiSCJIIjQiNCHEIcQhDCEMICAgICL0HvQdkB2QHAAcAB40GjQYNBg0GhQWFBfME8wRZBFkEuAO4Aw8DDwNeAl4CpgGmAeoA6gArACsAbP9s/7P+s/4B/gH+Vv1W/bb8tvwd/B38hvuG+/b69vps+mz66Pno+XH5cfkH+Qf5qPio+Fr4Wvga+Br44/fj9773vvel96X3lfeV95H3kfeV95X3mfeZ96b3pve597n30ffR9/T39Pch+CH4U/hT+Iz4jPjL+Mv4DPkM+VL5Uvmb+Zv54fnh+Sb6Jvpn+mf6ofqh+tr62voW+xb7VPtU+5r7mvvk++T7Lvwu/Hj8ePzB/MH8B/0H/U79Tv2W/Zb94f3h/S/+L/6B/oH+2f7Z/jf/N/+d/53/CgAKAHcAdwDhAOEARgFGAaIBogH6AfoBUQJRAqcCpwL/Av8CWQNZA64DrgP+A/4DSARIBIoEigTKBMoEDwUPBVUFVQWgBaAF7gXuBTYGNgZ3BncGtAa0BukG6QYcBxwHVAdUB4gHiAe4B7gH4wfjBwIIAggaCBoIMggyCEcIRwhZCFkIZghmCGIIYghHCEcIGQgZCNEH0QduB24H8AbwBlEGUQaJBYkFnAScBI4DjgNnAmcCOAE4AQ0ADQDy/vL+7P3s/Qb9Bv0+/D78kfuR+wD7APuB+oH6CvoK+pr5mvkt+S35v/i/+Fb4Vvjs9+z3evd69wf3B/eT9pP2JPYk9sv1y/WL9Yv1XPVc9UH1QfUq9Sr1D/UP9fn0+fTs9Oz06PTo9Pf09/QQ9RD1KPUo9UP1Q/Vf9V/1evV69Zv1m/W69br1y/XL9dX11fXe9d718vXy9Sr2KvaQ9pD2G/cb98b3xveF+IX4T/lP+Sn6KfoX+xf7EvwS/BH9Ef0G/gb+5f7l/qz/rP9pAGkAKAEoAfIB8gHLAssCtAO0A6MEowSdBZ0FpwanBrcHtwfNCM0I4wnjCeUK5QrZC9kLxgzGDKYNpg2DDoMOYQ9hDykQKRDbENsQexF7Ef4R/hFxEnES5RLlEkwTTBOxE7ETHRQdFIAUgBTiFOIUThVOFbEVsRUQFhAWcRZxFr0WvRb2FvYWIBcgFyMXIxcCFwIXyhbKFnEWcRYGFgYWnRWdFSsVKxW3FLcUSRRJFNIT0hNYE1gT4RLhEl8SXxLREdERPRE9EZcQlxDnD+cPNw83D4AOgA7DDcMNAw0DDTQMNAxaC1oLgQqBCqoJqgndCN0IIAggCG0HbQfBBsEGHwYfBoMFgwXuBO4EZARkBN4D3gNaA1oD0wLTAkMCQwKmAaYB+AD4ADwAPABx/3H/n/6f/s79zv0C/QL9Ofw5/Hf7d/u2+rb68vny+TT5NPl9+H34yPfI9x33Hfd09nT2xfXF9R31HfV59Hn02vPa80nzSfPA8sDyM/Iz8rHxsfE88Tzx1/DX8Jfwl/B38HfwaPBo8Gvwa/B18HXwf/B/8JzwnPDO8M7wDPEM8VfxV/Gf8Z/x1PHU8QLyAvIt8i3yWfJZ8pnymfLt8u3yT/NP88nzyfNd9F30B/UH9cz1zPWj9qP2efd590f4R/gF+QX5tPm0+V/6X/oO+w77vvu++2r8avwK/Qr9lf2V/Q3+Df58/nz+5/7n/lT/VP+//7//JAAkAHsAewDFAMUACAEIAUoBSgGOAY4B0gHSAQ8CDwI+Aj4CXgJeAm4CbgJzAnMCcAJwAmUCZQJWAlYCRwJHAj4CPgJEAkQCXwJfAogCiAK2ArYC3QLdAvUC9QL+Av4CAgMCAwcDBwMSAxIDIAMgAykDKQMqAyoDJgMmAyEDIQMfAx8DIwMjAyQDJAMbAxsDBgMGA+QC5AK7ArsCkwKTAmsCawI/Aj8CDQINAtQB1AGUAZQBUQFRAQwBDAHCAMIAbgBuABEAEQCv/6//Uf9R/wH/Af/E/sT+mP6Y/nz+fP5o/mj+WP5Y/kn+Sf43/jf+Hv4e/vz9/P3R/dH9nP2c/WL9Yv0k/ST94/zj/KT8pPxq/Gr8Nvw2/BD8EPz1+/X73vve+8f7x/ut+637jfuN+3L7cvtg+2D7WPtY+1z7XPtk+2T7Z/tn+2b7Zvtg+2D7UvtS+z37Pfsf+x/79vr2+sz6zPqo+qj6kfqR+o76jvqa+pr6qvqq+rn6ufrB+sH6wfrB+sD6wPq7+rv6r/qv+pz6nPp8+nz6TvpO+hz6HPro+ej5tPm0+Yf5h/lc+Vz5L/kv+Qj5CPnj+OP4v/i/+KP4o/iN+I34f/h/+IH4gfiW+Jb4vPi8+PX49fg2+Tb5dvl2+bL5svnl+eX5DvoO+jj6OPpn+mf6n/qf+uX65fo6+zr7mPuY+/77/vto/Gj80PzQ/Db9Nv2X/Zf97f3t/Tb+Nv5v/m/+l/6X/rT+tP7M/sz+5f7l/gH/Af8h/yH/Qv9C/2D/YP97/3v/kv+S/6j/qP+//7//2v/a//z//P8nACcAXABcAJcAlwDXANcAFwEXAVUBVQGOAY4BxQHFAfgB+AEoAigCVQJVAnoCegKXApcCrwKvAsQCxALbAtsC+QL5Ah0DHQNFA0UDbgNuA5MDkwOuA64DwAPAA8QDxAO9A70DsQOxA6ADoAOSA5IDjQONA5EDkQOdA50DrgOuA7kDuQO6A7oDsgOyA6UDpQOeA54DqAOoA8MDwwPqA+oDFQQVBDcENwROBE4EXwRfBGwEbAR+BH4ElwSXBK8ErwTGBMYE4ATgBP0E/QQiBSIFUQVRBX8FfwWqBaoF0gXSBfQF9AUYBhgGQAZABmIGYgZzBnMGawZrBkQGRAb/Bf8FogWiBS0FLQWdBJ0E9gP2Az4DPgOJAokC8QHxAYQBhAFBAUEBGwEbAfsA+wDSANIAnwCfAGsAawA8ADwAEgASAN//3/+T/5P/Jv8m/6H+of4T/hP+jf2N/Rz9HP29/L38Z/xn/Bb8FvzJ+8n7hfuF+1L7Uvsu+y77DfsN++b65vqv+q/6Zfpl+hb6FvrL+cv5hvmG+Uv5S/kO+Q75w/jD+HD4cPgd+B342PfY97n3uffE98T38vfy9zn4OfiJ+In42vja+Db5Nvmj+aP5I/oj+q76rvoy+zL7mvua++X75fsd/B38VfxV/KD8oPwG/Qb9gP2A/Qb+Bv6T/pP+LP8s/9f/1/+ZAJkAaAFoATACMALnAucCiQOJAx4EHgS2BLYEXgVeBQgGCAanBqcGLwcvB5MHkwffB98HKQgpCHgIeAjYCNgISglKCcEJwQk+Cj4KygrKClwLXAv5C/kLoQyhDD0NPQ3KDcoNSg5KDrAOsA4GDwYPUw9TD40PjQ+7D7sP6A/oDxAQEBA/ED8QghCCEMsQyxAYERgRaBFoEaURpRHSEdIR9hH2EQUSBRIHEgcSBxIHEvMR8xHOEc4RmRGZEUURRRHVENUQVRBVEMEPwQ8jDyMPiA6IDuoN6g1LDUsNsgyyDBgMGAyCC4IL+Ar4CnIKcgrsCewJZglmCdII0ggqCCoIcAdwB6IGogbCBcIF2gTaBPAD8AMJAwkDKgIqAlcBVwGNAI0Ayv/K/w7/Dv9X/lf+o/2j/fX89fxI/Ej8mfuZ++z67Po9+j36ifmJ+dj42Pgk+CT4afdp9672rvbw9fD1LPUs9XD0cPS887zzC/ML823ybfLj8ePxbfFt8R3xHfHy8PLw4PDg8Ofw5/D08PTw8fDx8OXw5fDM8MzwqPCo8JHwkfCM8Izwl/CX8LzwvPD08PTwMvEy8X7xfvHV8dXxNvI28q/yr/I+8z7z2fPZ8370fvQf9R/1r/Wv9TT2NPau9q72Ivci95j3mPcP+A/4g/iD+PP48/he+V75wfnB+SL6IvqF+oX66frp+lj7WPvY+9j7bvxu/Br9Gv3V/dX9jP6M/i3/Lf+u/67/DQANAFUAVQCVAJUA2gDaACYBJgF0AXQBvwG/AQACAAI6AjoCdQJ1ArYCtgIDAwMDXQNdA7sDuwMTBBMEXwRfBJIEkgSrBKsErgSuBJ8EnwSHBIcEbwRvBFUEVQQ2BDYEEAQQBNsD2wOdA50DXgNeAyUDJQP6AvoC4wLjAtsC2wLfAt8C6gLqAvIC8gLuAu4C1wLXAqYCpgJeAl4CCAIIArEBsQFhAWEBGgEaAdYA1gCMAIwAOAA4AN3/3f+F/4X/Ov86/wD/AP/O/s7+lf6V/kn+Sf7k/eT9af1p/ev86/xy/HL8BfwF/Kb7pvtN+0379Pr0+p/6n/pP+k/6BfoF+sX5xfmP+Y/5Xfld+Tj5OPki+SL5Gvka+SP5I/kz+TP5O/k7+Tf5N/kj+SP5/vj++Nn42fi4+Lj4nfid+JD4kPiM+Iz4j/iP+J/4n/i8+Lz44Pjg+Az5DPk2+Tb5U/lT+Wb5Zvlt+W35b/lv+Xn5efmQ+ZD5r/mv+dT51Pny+fL5/vn++f/5//kB+gH6EvoS+j/6P/qG+ob61vrW+iH7Iftb+1v7h/uH+7L7svvo++j7LPws/Hj8ePy+/L789fz1/B79Hv1A/UD9YP1g/YX9hf2w/bD93f3d/Q7+Dv5D/kP+e/57/rL+sv7j/uP+DP8M/zX/Nf9m/2b/pv+m/+//7/81ADUAbABsAI8AjwCjAKMAtQC1AMoAygDiAOIA/AD8ABoBGgFJAUkBlwGXAf8B/wFtAm0CwQLBAuIC4gLVAtUCugK6ArcCtwLiAuICNAM0A4kDiQPCA8ID2APYA9UD1QPTA9MD3wPfA+wD7APqA+oD1QPVA7cDtwOpA6kDvQO9A+cD5wMQBBAEJAQkBBwEHAQMBAwEDQQNBCEEIQQ7BDsERwRHBDcENwQcBBwEFAQUBCwELAReBF4EkQSRBKcEpwSeBJ4EiwSLBIYEhgSdBJ0EyATIBO8E7wQLBQsFJgUmBU0FTQWNBY0F2wXbBRUGFQYoBigGFgYWBvEF8QXaBdoF5AXkBQQGBAYoBigGRAZEBlQGVAZkBmQGewZ7Bo0GjQaOBo4GeQZ5BlIGUgYvBi8GJwYnBjUGNQZKBkoGVAZUBkcGRwYtBi0GGAYYBgkGCQb5BfkF3AXcBacFpwViBWIFJAUkBfcE9wTaBNoEwgTCBJsEmwRhBGEEGAQYBMMDwwNaA1oD0wLTAiUCJQJYAVgBhwCHANP/0/9K/0r/4/7j/oj+iP4f/h/+n/2f/Rf9F/2R/JH8E/wT/Jn7mfsV+xX7gvqC+u357fli+WL56Pjo+IP4g/gq+Cr41PfU94z3jPdP90/3GPcY9+H24fae9p72R/ZH9vT19PW99b31r/Wv9dT11PUW9hb2WfZZ9pT2lPbC9sL24vbi9vv2+/YL9wv3DfcN9xP3E/cy9zL3fPd89/z3/Pef+J/4Q/lD+dX51flS+lL6yvrK+lb7Vvv3+/f7m/yb/Cn9Kf2Z/Zn9+f35/Wj+aP4A/wD/wf/B/5AAkABQAVAB8QHxAXcCdwL2AvYCfwN/Aw8EDwSfBJ8ELgUuBbsFuwVaBloGFwcXB+IH4gekCKQIRglGCbEJsQn0CfQJLAosCmkKaQq7CrsKIQshC4gLiAvtC+0LVwxXDMAMwAwtDS0NoA2gDQMOAw5VDlUOmg6aDsoOyg7tDu0ODg8ODyUPJQ84DzgPUw9TD2gPaA93D3cPfw9/D20PbQ9FD0UPFg8WD94O3g6lDqUObw5vDi0OLQ7iDeINmg2aDVINUg0ODQ4NxwzHDGAMYAzNC80LFAsUCz8KPwpqCWoJrAisCAIIAghfB18HtQa1BvsF+wU4BTgFegR6BMgDyAMeAx4DcwJzAr4BvgH8APwANwA3AHf/d/+//r/+C/4L/lr9Wv2q/Kr8/fv9+2D7YPvV+tX6VvpW+uH54flu+W75+/j7+JX4lfhD+EP4BPgE+N733vfD98P3p/en94n3ifdh92H3MPcw9wD3APfW9tb2r/av9pX2lfaB9oH2avZq9lX2VfZE9kT2OfY59kL2QvZd9l32evZ69pX2lfaj9qP2n/af9p/2n/at9q32y/bL9vz2/PYu9y73TvdO92D3YPdn92f3bfdt94f3h/e397f39/f390b4Rvid+J347/jv+Dv5O/l4+Xj5oPmg+b35vfnZ+dn5APoA+kD6QPqU+pT67Prs+jr7Ovtw+3D7jfuN+5/7n/u1+7X72/vb+xr8Gvxt/G38y/zL/Cn9Kf18/Xz9u/27/eX95f3+/f79Dv4O/iD+IP44/jj+U/5T/m3+bf6F/oX+mv6a/rT+tP7T/tP+9P70/g3/Df8Z/xn/Hf8d/yj/KP9M/0z/kP+Q/+r/6v8/AD8AdAB0AHsAewBaAFoAJgAmAPn/+f/p/+n/+v/6/yUAJQBWAFYAdgB2AG0AbQAyADIA0//T/3H/cf82/zb/P/8//4j/iP/n/+f/IQAhAAgACACX/5f/9/73/mz+bP4s/iz+RP5E/o/+j/7M/sz+wP7A/lz+XP7B/cH9L/0v/ef85/wB/QH9Z/1n/dz93P0Z/hn+9f31/X39ff3n/Of8efx5/GT8ZPyl/KX8CP0I/U/9T/1K/Ur99vz2/Hz8fPwR/BH83Pvc++r76vsq/Cr8ffx9/MP8w/zf/N/8wPzA/Gv8a/z9+/37q/ur+637rfsT/BP8sfyx/DL9Mv1B/UH9xfzF/AD8APxk+2T7TvtO+877zvuQ/JD8Gv0a/SP9I/24/Lj8NPw0/P77/vs6/Dr8vPy8/DP9M/1f/V/9RP1E/R39Hf0i/SL9Xv1e/a79rv3i/eL96f3p/eL94v38/fz9Tf5N/r3+vf4a/xr/Pf89/yz/LP8W/xb/LP8s/3z/fP/t/+3/TQBNAHkAeQBtAG0AQQBBABcAFwAFAAUAFAAUAEUARQCNAI0A3wDfACABIAE6AToBKwErAQcBBwH3APcAGQEZAWYBZgG2AbYB3wHfAc8BzwGmAaYBlgGWAb0BvQENAg0CWAJYAnUCdQJtAm0CcQJxArICsgI2AzYDyAPIAxkEGQQDBAMEpgOmA1MDUwNYA1gDwwPDA1MEUwSwBLAErASsBF0EXQQPBA8EBwQHBEoESgSpBKkE6AToBPAE8ATnBOcEDAUMBXkFeQUSBhIGjwaPBqYGpgZSBlIGzwXPBXMFcwWABYAF+wX7BaQGpAYsBywHYwdjBz4HPgfpBukGnQadBnwGfAaTBpMG2AbYBi0HLQd+B34Hvwe/B9gH2AfAB8AHeQd5BwoHCgebBpsGXAZcBmUGZQavBq8GDQcNBzMHMwfzBvMGVQZVBo8FjwXxBPEErQStBLMEswTDBMMEmgSaBBsEGwRoA2gDwQLBAlUCVQIjAiMCBQIFAtYB1gGOAY4BSAFIARoBGgH1APUApwCnAAkACQAs/yz/YP5g/gj+CP5K/kr+5v7m/lf/V/8k/yT/Nf41/uz87Pzd+937bvtu+6D7oPsT/BP8XPxc/FL8UvwT/BP82fvZ+8D7wPul+6X7U/tT+8X6xfo2+jb6//n/+Vv6W/of+x/70/vT+wr8Cvyh+6H72frZ+jD6MPoA+gD6TfpN+tz63Ppb+1v7nvue+677rvum+6b7k/uT+3X7dftM+0z7Nvs2+2T7ZPv1+/X7zvzO/KP9o/0f/h/+If4h/tH90f2H/Yf9iv2K/er96v2B/oH+H/8f/6//r/85ADkAxADEADgBOAFnAWcBOQE5Ac0AzQCAAIAArwCvAHEBcQGAAoACZANkA8EDwQOaA5oDSQNJAy4DLgN1A3UD8wPzA08ETwRaBFoEMAQwBBoEGgRWBFYE4QThBHAFcAW3BbcFpAWkBV8FXwUyBTIFRgVGBYAFgAWsBawFrAWsBZEFkQWXBZcF6gXqBW8GbwbdBt0G6wbrBoIGggbeBd4FXgVeBT0FPQV6BXoF2QXZBRQGFAYPBg8G3QXdBZ0FnQVcBVwFFAUUBbEEsQRABEAE6gPqA9ID0gP+A/4DQQRBBFQEVAQMBAwEdAN0A8ICwgI0AjQC5QHlAcABwAGgAaABbwFvATYBNgEQARABDQENARQBFAH5APkAmACYAPf/9/9G/0b/w/7D/pb+lv66/rr+A/8D/zz/PP9A/0D/A/8D/5X+lf4P/g/+jf2N/S39Lf0C/QL9EP0Q/Un9Sf2D/YP9lP2U/WL9Yv3y/PL8Y/xj/Of75/uh+6H7lvuW+7r7uvvx+/H7Ifwh/D78PvxE/ET8NPw0/Bf8F/zu++77vfu9+4z7jPth+2H7SPtI+1P7U/uL+4v77Pvs+2D8YPy4/Lj8x/zH/Hv8e/zt++37W/tb+xP7E/tB+0H71vvW+5X8lfwn/Sf9TP1M/fv8+/xd/F38uPu4+1f7V/tj+2P71/vX+4v8i/wz/TP9hP2E/VH9Uf2o/Kj81PvU+0n7Sftj+2P7Kvwq/EX9Rf0e/h7+Nv42/nz9fP1k/GT8ovui+8r7yvvk/OT8Yf5h/nD/cP+A/4D/mP6Y/l39Xf2g/KD83Pzc/OX95f0K/wr/jf+N/yj/KP82/jb+dv12/YP9g/1k/mT+iP+I/yoAKgDf/9//4f7h/u797v29/b39hf6F/tX/1f/fAN8ABQEFATsAOwAH/wf/Gf4Z/tr92v0y/jL+tv62/gf/B/8P/w//Bf8F/yT/JP9s/2z/oP+g/3//f/8K/wr/kv6S/oD+gP73/vf+rf+t/xcAFwDR/9H/8v7y/gv+C/7F/cX9aP5o/qr/qv/VANUASgFKAdsA2wDf/9//5/7n/lv+W/5F/kX+af5p/pD+kP6r/qv+0/7T/hL/Ev9E/0T/Kf8p/6b+pv7t/e39bv1u/Y39jf1Q/lD+Sf9J/9//3/+s/6z/xv7G/rD9sP39/P388vzy/Gf9Z/31/fX9SP5I/lv+W/5l/mX+mP6Y/ur+6v4Q/xD/v/6//vP98/0I/Qj9jfyN/Oz87PwW/hb+hP+E/3wAfAB3AHcAdP90///9//3l/OX8x/zH/MT9xP1o/2j/6gDqAJYBlgEqASoB9f/1/5/+n/7K/cr9wv3C/WD+YP49/z3/9//3/2sAawCoAKgAyADIALsAuwBbAFsAlv+W/6b+pv4K/gr+Ov46/kv/S//BAMEAzgHOAc4BzgG8ALwARf9F/13+Xf6g/qD+7//v/4ABgAFpAmkCOAI4AjUBNQEkACQAu/+7/yIAIgDwAPAAgQGBAXIBcgHrAOsAcgByAIEAgQAqASoBCwILApUClQJ8AnwC6QHpAVsBWwFHAUcByQHJAYICggLvAu8CvQK9AggCCAJWAVYBNwE3AeYB5gEfAx8DSARIBMUExQRiBGIEbgNuA4UChQI+Aj4CzQLNAu0D7QMMBQwFogWiBXAFcAWlBKUEvgO+AzcDNwNeA14DLwQvBFkFWQVvBm8GFAcUBxIHEgd6BnoGkwWTBbcEtwQ8BDwETgROBNwE3ASmBaYFVQZVBpcGlwZPBk8GmwWbBcYExgQqBCoECwQLBGYEZgT1BPUEUAVQBRwFHAVQBFAEQQNBA3sCewJuAm4CJAMkAyUEJQS/BL8EcQRxBEEDQQPCAcIBvgC+AKwArABqAWoBUAJQAqQCpAIbAhsCBgEGAQ4ADgC3/7f/BAAEAHsAewCGAIYA6f/p//D+8P4v/i/+F/4X/qP+o/5c/1z/sP+w/1b/Vv9u/m7+W/1b/X/8f/wA/AD80fvR+9373fsa/Br8gfyB/Pj8+Pw8/Tz9CP0I/VP8U/xn+2f7vvq++rr6uvpQ+1D7DfwN/G/8b/w7/Dv8q/ur+0r7Svt5+3n7I/wj/M38zfzw/PD8a/xr/LP7s/t5+3n7Lfwt/Jv9m/35/vn+aP9o/43+jf7e/N78Yvti+xf7F/tS/FL8hv6G/pkAmQCFAYUB7wDvAFT/VP+z/bP9/fz9/Iz9jP0E/wT/kgCSAHkBeQF4AXgB4wDjAF0AXQBjAGMA+gD6AK0BrQHwAfABgwGDAbYAtgA6ADoArQCtAB4CHgLxA/EDMgUyBSUFJQXPA88D/QH9AckAyQD2APYAdAJ0AmgEaAS+Bb4FzQXNBbIEsgRAA0ADZgJmAqICogK6A7oD9gT2BI0FjQUtBS0FFwQXBOkC6QJBAkECagJqAj4DPgNKBEoECwULBSAFIAV0BHQEQANAA/wB/AEuAS4BMwEzAQICAgIkAyQD6gPqA8wDzAPGAsYCZwFnAXsAewCCAIIAWAFYAUICQgJ1AnUCqAGoAVQAVABg/2D/ef95/4sAiwDAAcABHAIcAjIBMgF6/3r/Bf4F/rn9uf2m/qb+/P/8/54AngDx//H/Sv5K/rb8tvw4/Dj8HP0c/cH+wf4OAA4AKQApAAD/AP9B/UH93fvd+3n7efsT/BP8Jv0m/Qb+Bv5G/kb+4f3h/S79Lv2c/Jz8ePx4/Mv8y/xV/VX9r/2v/Y/9j/3z/PP8MPww/ND70Psr/Cv8KP0o/Tj+OP6k/qT+Cf4J/q/8r/xd+1374frh+or7ivvn/Of8Hf4d/nj+eP7c/dz9yfzJ/PP78/vB+8H7G/wb/KH8ofz//P/8L/0v/V/9X/2j/aP9xv3G/Xb9dv2Y/Jj8hfuF+/X69fp0+3T78fzx/K/+r/6t/63/V/9X//D98P1m/Gb8s/uz+zv8O/yQ/ZD9zf7N/j7/Pv/S/tL+Ev4S/rT9tP0R/hH+8/7z/sn/yf8aABoA0v/S/0f/R//v/u/+Ef8R/5f/l/8sACwAeAB4AFwAXAD7//v/pP+k/5v/m//v/+//cgByANAA0ADJAMkAWwBbANf/1/+r/6v/IgAiABoBGgEHAgcCQgJCAnsBewEHAAcAxf7F/pP+k/6z/7P/hgGGAdsC2wLBAsECMwEzATb/Nv83/jf+EP8Q/1cBVwGYA5gDPQQ9BK8CrwLU/9T/gv2C/Uz9TP1k/2T/dQJ1AnoEegQoBCgExwHHAQf/B//H/cf90P7Q/jwBPAEyAzIDNQM1A0gBSAHk/uT+6/3r/S7/Lv/MAcwBxAPEA20DbQPFAMUAhf2F/f77/vtj/WP95ADkAB4EHgTNBM0EXAJcAl/+X/58+3z7k/uT+3P+c/4IAggC2gPaA88CzwLd/93/QP1A/cX8xfyA/oD+1QDVAMsBywGeAJ4AVv5W/vn8+fzi/eL9pACkAD0DPQOVA5UDFAEUATf9N/2H+of61PrU+t793v2LAYsBXANcAzoCOgIo/yj/cPxw/PT79PvU/dT9eAB4ANMB0wHzAPMApP6k/sX8xfzN/M38sP6w/vUA9QDjAeMB0gDSAK/+r/42/Tb9mv2a/ZL/kv+QAZAB7AHsATcANwCc/Zz9EvwS/PL88vzq/+r/JwMnA4UEhQQUAxQDsf+x/4f8h/yT+5P7ZP1k/b4AvgB/A38DAwQDBDICMgJr/2v/iv2K/Zb9lv0w/zD/8ADwAIwBjAHJAMkAnP+c/1L/Uv92AHYARwJHAjEDMQMaAhoCVP9U/638rfw2/Db8xf7F/h8DHwOmBqYGCQcJB+MD4wMo/yj/5fvl+z/8P/z+//7/vQS9BIEHgQfGBsYGYwNjA9b/1v99/n3+AgACABADEAN2BXYFxgXGBTwEPARZAlkCoAGgAV8CXwKnA6cDJQQlBFgDWAMAAgACiAGIAcUCxQIwBTAFHQcdB+cG5wY+BD4EhACEAP39/f1R/lH+cAFwAYQFhAUdCB0IsQexB3sEewRPAE8AYv1i/Qr9Cv0U/xT/IgIiApQElARvBW8FsASwBPgC+AIMAQwBZ/9n/z7+Pv62/bb9AP4A/jr/Ov8lASUBBAMEA9gD2AP2AvYCggCCAIL9gv1l+2X7Mvsy+/n8+fy7/7v/AQIBAqsCqwJ+AX4BLv8u/+X85fyY+5j7l/uX+438jfzG/cb9n/6f/s/+z/5n/mf+sf2x/fn8+fx4/Hj8S/xL/Ib8hvwo/Sj9Dv4O/ub+5v43/zf/mv6a/gb9Bv0F+wX7j/mP+af5p/mj+6P72/7b/swBzAHaAtoCQQFBAaL9ov3K+cr5svey92z4bPh/+3/7Q/9D/+YB5gFqAmoCFQEVAQD/AP9L/Uv9dPx0/Fb8VvyX/Jf8I/0j/TT+NP7m/+b/1AHUAREDEQPAAsACxADEAAj+CP4Y/Bj8Kfwp/FX+Vf57AXsB9wP3A5YElgRIA0gDAAEAAQn/Cf9H/kf+2/7b/ksASwDtAe0BQQNBA/cD9wPtA+0DHgMeA78BvwFXAFcAm/+b/wkACQCNAY0BbQNtA6sEqwSiBKIEcQNxA+EB4QHhAOEA5QDlAKoBqgF/An8C0gLSApACkAIdAh0C7wHvARwCHAJSAlICJgImAngBeAGmAKYARQBFALIAsgC/Ab8BygLKAiADIAN/An8CPQE9ARIAEgCh/6H/EwATAAkBCQHaAdoBAAIAAmMBYwFkAGQAn/+f/5D/kP9EAEQAUQFRAQYCBgLaAdoBugC6ACf/J//1/fX91P3U/eP+4/6LAIsA1QHVAe4B7gGxALEAvP68/h/9H/21/LX8rf2t/Wv/a//tAO0AXgFeAY4AjgD8/vz+g/2D/db81vwl/SX9Hv4e/jX/Nf/0//T/NQA1AAoACgCY/5j/7/7v/hn+Gf49/T39q/yr/ML8wvyl/aX9Ef8R/2kAaQADAQMBkgCSAE3/Tf/Q/dD9vfy9/Gv8a/zM/Mz8lv2W/X7+fv5R/1H/5f/l/wsACwCe/57/rv6u/qn9qf0k/ST9hP2E/Zr+mv6n/6f/0P/Q/8n+yf4p/Sn9FvwW/ID8gPxd/l3+mgCaAMgByAEdAR0BB/8H/+j86Pwb/Bv8Bf0F/dj+2P46ADoARwBHADX/Nf8X/hf+A/4D/jX/Nf/lAOUA3AHcAVsBWwGm/6b/yv3K/d/83/xS/VL9uf65/jUANQAXARcBOQE5AeoA6gCQAJAAUQBRABQAFAC2/7b/Pf89/+P+4/7f/t/+O/87/8X/xf85ADkAbgBuAGcAZwBDAEMAGAAYAO//7//H/8f/q/+r/6r/qv/H/8f/5P/k/9j/2P+b/5v/Wv9a/2j/aP/8//z/8wDzANgB2AEcAhwCgAGAATwAPADo/uj+If4h/jn+Of4Z/xn/awBrAMUBxQHKAsoCKQMpA6ECoQIuAS4BNv82/4v9i/0U/RT9Qv5C/qUApQANAw0DMgQyBIcDhwOQAZABk/+T/6/+r/4r/yv/VwBXADgBOAFGAUYB0QDRAJoAmgARAREB5QHlATYCNgJkAWQBr/+v/zL+Mv4P/g/+jv+O/8MBwwEvAy8D0QLRAugA6ADK/sr+7f3t/d/+3/7pAOkAoQKhAukC6QKxAbEB4P/g/6j+qP6w/rD+zP/M/zsBOwE7AjsCcgJyAvoB+gElASUBRgBGAJr/mv9S/1L/j/+P/04ATgBGAUYB/wH/AQwCDAJbAVsBQwBDAFn/Wf8R/xH/f/9//1kAWQAwATABtQG1AdUB1QGeAZ4BIwEjAW4AbgCb/5v/7P7s/rr+uv5F/0X/eAB4ANwB3AHDAsMCpQKlAmcBZwFz/3P/hv2G/WD8YPxt/G38ov2i/YT/hP9WAVYBZgJmAlQCVAI2ATYBkv+S/yH+If5u/W79lf2V/UP+Q/7w/vD+Nv82/xX/Ff/h/uH+Af8B/6D/oP9/AH8AIQEhARQBFAE+AD4A8f7x/sT9xP1H/Uf9uP24/en+6f5bAFsAfQF9AfUB9QG/Ab8BJgEmAYUAhQARABEAuv+6/0z/TP+8/rz+UP5Q/pb+lv7w//D/LgIuAmMEYwRYBVgFRAREBHEBcQE+/j7+XPxc/Mn8yfwu/y7/DgIOAswDzAOwA7ADTwJPAv0A/QC9AL0AhAGEAVECUQISAhICjgCOAKf+p/67/bv9lP6U/ssAywAMAwwDBwQHBFsDWwPEAcQBfAB8AEkASQD6APoAqAGoAYEBgQFuAG4AIf8h/4r+iv4p/yn/uAC4AG4CbgJ3A3cDaQNpA1ECUQKSAJIAo/6j/gX9Bf09/D38rfyt/GT+ZP7bANsAGAMYAxUEFQRLA0sDDgEOAVr+Wv5I/Ej8dPt0+8v7y/vG/Mb83P3c/dz+3P7U/9T/1QDVAKwBrAHjAeMBFwEXAUf/R//u/O780PrQ+qT5pPnH+cf5I/sj+1L9Uv29/73/wwHDAdcC1wKZApkCBwEHAYr+iv7n++f79fn1+VX5Vfku+i76L/wv/MD+wP4uAS4B4QLhAmwDbAOdAp0CnQCdAPj9+P2S+5L7TPpM+q36rfqP/I/8K/8r/4IBggHVAtUC+AL4AkACQAIoASgBBAAEAOD+4P7A/cD92PzY/KX8pfyf/Z/90P/Q/5UClQLNBM0EawVrBQwEDARDAUMBUP5Q/nv8e/xu/G789f31/TMAMwAzAjMCWwNbA5kDmQM5AzkDlwKXAuMB4wEkASQBXABcAK3/rf9T/1P/gf+B/zMAMwAtAS0BGAIYArICsgLrAusC1wLXAocChwL2AfYBFQEVAfX/9f/o/uj+bv5u/vH+8f5qAGoATgJOAr0DvQP/A/8D9QL1AjIBMgGm/6b/FP8U/5v/m/+1ALUAlwGXAboBugEsASwBbwBvAAkACQAkACQAfQB9AKwArACGAIYAQwBDAEMAQwCsAKwANQE1AU4BTgGXAJcAQP9A///9//2d/Z39ZP5k/uP/4/86AToBrQGtASEBIQEUABQAMP8w/8T+xP6d/p3+Uf5R/rv9u/00/TT9U/1T/W7+bv4xADEAvgG+ATwCPAJlAWUBuv+6/yX+Jf5S/VL9Qv1C/Wz9bP1C/UL9svyy/Ef8R/y6/Lr8V/5X/qcApwCUApQCEwMTA7wBvAET/xP/Pvw+/HT6dPpb+lv60fvR+xX+Ff4yADIAbgFuAZABkAHPAM8Apv+m/43+jf7I/cj9W/1b/TL9Mv05/Tn9dv12/fr9+v2+/r7+j/+P/xkAGQAeAB4Aov+i//j++P6M/oz+lv6W/vD+8P44/zj/H/8f/7/+v/6I/oj+9/73/hgAGABkAWQBCAIIAn4BfgH6//r/X/5f/qv9q/1M/kz+1f/V/08BTwHrAesBkAGQAdAA0ABlAGUAkwCTAPoA+gDzAPMAMQAxABn/Gf98/nz++P74/moAagADAgMC1wLXAo4CjgKaAZoB1QDVAMYAxgBAAUABjwGPARsBGwH4//j/6v7q/sr+yv7i/+L/owGjAQkDCQNPA08DcgJyAhgBGAEPAA8AuP+4/+P/4/8gACAAMQAxADYANgB7AHsAFgEWAb4BvgH8AfwBmQGZAdQA1AA7ADsANgA2AKsAqwAZARkB+QD5ADsAOwBa/1r///7//oP/g/+oAKgAyQHJAVUCVQIpAikCkAGQAe0A7QBrAGsA7//v/1r/Wv/N/s3+q/6r/k3/Tf+aAJoA/QH9Aa0CrQJAAkAC9wD3AJn/mf/x/vH+RP9E/zEAMQAIAQgBSgFKAf4A/gCTAJMAeAB4AMEAwQAeAR4BLQEtAc0AzQBDAEMAAAAAAEUARQDsAOwAhAGEAaQBpAE9AT0BnwCfADkAOQBGAEYApgCmAP0A/QAEAQQBvwC/AHkAeQCGAIYA+wD7AJkBmQH1AfUBxAHEARQBFAFDAEMAxv/G/9n/2f9oAGgAHAEcAaEBoQHOAc4BtAG0AXcBdwEpASkBwQDBADIAMgCW/5b/LP8s/0H/Qf/q/+r/6wDrAMcBxwEKAgoCkQGRAaEAoQC6/7r/Qf9B/0b/Rv+I/4j/rv+u/5P/k/9Z/1n/Rf9F/4D/gP/u/+7/QwBDAD0APQDV/9X/S/9L//D+8P7q/ur+HP8c/z3/Pf8V/xX/pf6l/ir+Kv7z/fP9Kf4p/rn+uf5i/2L/3//f/wsACwDp/+n/l/+X/zT/NP/Y/tj+jP6M/l7+Xv5e/l7+lP6U/vn++f5x/3H/1f/V/woACgALAAsA7P/s/8f/x/+o/6j/jf+N/2z/bP9E/0T/Jv8m/yz/LP9j/2P/uP+4/wcABwAqACoAFQAVAN7/3v+n/6f/iv+K/4r/iv+b/5v/s/+z/9T/1P8IAAgARwBHAHoAegCAAIAAUABQAAMAAwDE/8T/sf+x/7//v//L/8v/uf+5/57/nv+u/67/EQARAKsAqwAcARwB+gD6ACcAJwD+/v7+KP4o/ir+Kv4N/w3/SwBLACoBKgE4ATgBkgCSAL//v/9I/0j/Vf9V/6H/of/B/8H/g/+D/xD/EP/G/sb+3/7f/kr/Sv+6/7r/8P/w/+T/5P+8/7z/nf+d/4L/gv9I/0j/4v7i/nn+ef5V/lX+mv6a/iD/IP+E/4T/eP94/w7/Dv+u/q7+w/7D/lr/Wv8IAAgAOgA6AK//r/+x/rH+6P3o/eD94P2Z/pn+kP+Q/ycAJwAcABwArP+s/1P/U/9d/13/qP+o/9b/1v+j/6P/Kv8q/8r+yv7S/tL+Of85/7H/sf/r/+v/5f/l/97/3v8VABUAewB7ALgAuAB9AH0A3P/c/0n/Sf9I/0j/9P/0/9gA2ABGAUYB5QDlAAIAAgBc/1z/j/+P/48AjwCsAawBHgIeAqQBpAG0ALQAHAAcAEsASwAEAQQBjwGPAWABYAGUAJQA3P/c/+n/6f/PAM8A6gHqAWECYQLXAdcBuQC5AOj/6P8JAAkAAgECARACEAJrAmsC3QHdAd8A3wAyADIAOwA7AL8AvwAsASwBHwEfAbkAuQB8AHwAyQDJAHwBfAEGAgYC3wHfAQMBAwH8//z/eP94/8D/wP+CAIIAFQEVAQQBBAFnAGcAxf/F/5r/mv/3//f/eAB4AKQApABaAFoA5v/m/7n/uf/+//7/bwBvAIsAiwAGAAYAEP8Q/zf+N/77/fv9bf5t/iv/K/+s/6z/q/+r/1T/VP8M/wz/Hf8d/3f/d//G/8b/u/+7/0z/TP+2/rb+Qf5B/g3+Df4D/gP+/f39/fX99f0R/hH+d/53/iL/Iv/P/8//JwAnAP7//v91/3X/3/7f/of+h/5+/n7+nf6d/rH+sf6m/qb+l/6X/qn+qf7m/ub+Lv8u/1b/Vv9N/03/KP8o/xP/E/8f/x//N/83/zH/Mf/8/vz+tf61/pr+mv7S/tL+Sf9J/7T/tP/G/8b/af9p/9b+1v5u/m7+dv52/vD+8P6W/5b/EwATADoAOgAYABgA4P/g/7n/uf+r/6v/of+h/4j/iP9n/2f/Wv9a/33/ff/Q/9D/NgA2AIoAigC1ALUAvAC8ALEAsQCjAKMAkgCSAHcAdwBPAE8AJgAmAA8ADwAZABkARQBFAIQAhADHAMcAAQEBAS4BLgFHAUcBQgFCARQBFAG8ALwAUwBTAPz//P/b/9v/AQABAFwAXADEAMQAEAEQASEBIQH1APUAowCjAEsASwAPAA8ABgAGADcANwCSAJIA9wD3AD0BPQFHAUcBDgEOAaUApQAyADIA3P/c/7v/u//U/9T/HAAcAIAAgADpAOkARAFEAXoBegF/AX8BUAFQAf4A/gCmAKYAaABoAFQAVABoAGgAlACUAMIAwgDmAOYA/QD9AAgBCAEIAQgB/AD8AOQA5ADLAMsAvQC9AMEAwQDVANUA6QDpAO0A7QDeAN4AyQDJAMQAxADaANoAAwEDASMBIwEkASQBAQEBAc0AzQCsAKwAsgCyAN8A3wAXARcBPAE8ATsBOwEcARwB+QD5AOYA5gDtAO0AAQEBAQ8BDwEOAQ4BAgECAfwA/AAKAQoBLQEtAVIBUgFiAWIBUQFRASgBKAEEAQQBBgEGATgBOAGDAYMBvQG9AbkBuQFvAW8BAgECAa4ArgCkAKQA7ADsAFgBWAGjAaMBngGeAU0BTQHmAOYAqACoALAAsADiAOIABQEFAeoA6gCTAJMANQA1AAwADAAtAC0AdwB3AKcApwCMAIwAKwArAL3/vf+B/4H/j/+P/8j/yP/u/+7/2P/Y/4//j/9I/0j/OP84/2f/Z/+m/6b/tv+2/3T/dP/3/vf+g/6D/ln+Wf6H/of+5P7k/jD/MP87/zv/B/8H/7/+v/6Q/pD+iv6K/p3+nf6o/qj+m/6b/oL+gv54/nj+kf6R/sb+xv76/vr+Dv8O//n++f7O/s7+qf6p/qL+ov63/rf+0/7T/uD+4P7V/tX+vf69/rH+sf7D/sP+9f71/jb/Nv9q/2r/ev96/2H/Yf8u/y7//f79/uv+6/4E/wT/QP9A/4b/hv+5/7n/zP/M/8b/xv+6/7r/uf+5/8D/wP/A/8D/rP+s/4r/iv91/3X/jv+O/93/3f9FAEUAjwCPAIwAjAA6ADoAy//L/4b/hv+a/5r/+v/6/2QAZACJAIkASgBKAMr/yv9Z/1n/Pv8+/4f/h//+//7/VABUAFMAUwAEAAQAoP+g/2f/Z/9u/27/lv+W/6r/qv+M/4z/T/9P/yn/Kf9C/0L/lf+V/+j/6P/2//b/pP+k/xj/GP+j/qP+jv6O/uT+5P5q/2r/x//H/8D/wP9g/2D/6v7q/q7+rv7N/s3+JP8k/23/bf9x/3H/L/8v/9/+3/7D/sP+9P70/lH/Uf+X/5f/jv+O/z3/Pf/a/tr+q/6r/tL+0v4y/zL/iv+K/6H/of9x/3H/Jv8m//z+/P4Q/xD/UP9Q/4f/h/+N/43/Yv9i/y//L/8l/yX/Wf9Z/67/rv/y//L//////9r/2v+s/6z/o/+j/9D/0P8ZABkAUQBRAFcAVwAvAC8AAgACAPv/+/8tAC0AhACEANUA1QD8APwA7wDvAMcAxwClAKUApACkAMIAwgDsAOwADAEMARsBGwEhASEBLAEsAUQBRAFhAWEBdAF0AWoBagFEAUQBEwETAfIA8gD2APYAJAEkAWYBZgGcAZwBqgGqAYcBhwFDAUMB/gD+ANUA1QDTANMA7gDuAA8BDwEgASABGgEaAf8A/wDZANkAsACwAIcAhwBhAGEARwBHAD8APwBPAE8AbABsAIIAggB8AHwAVQBVABwAHADt/+3/4f/h//n/+f8gACAAMwAzAB8AHwDm/+b/o/+j/3b/dv9t/23/gf+B/5z/nP+o/6j/nf+d/4T/hP9q/2r/Vf9V/0X/Rf82/zb/Jv8m/x3/Hf8j/yP/OP84/1P/U/9m/2b/ZP9k/1D/UP8w/zD/Ev8S//3+/f70/vT+9P70/vv++/4G/wb/Ev8S/xz/HP8e/x7/G/8b/xj/GP8a/xr/I/8j/yj/KP8d/x3///7//tv+2/7H/sf+2P7Y/gz/DP9L/0v/c/9z/2//b/9G/0b/F/8X/wX/Bf8d/x3/T/9P/37/fv+V/5X/mP+Y/5r/mv+u/67/0P/Q/+z/7P/s/+z/z//P/63/rf+m/6b/zP/M/xEAEQBRAFEAawBrAFcAVwAtAC0AEwATAB0AHQBEAEQAbABsAH0AfQB1AHUAaABoAG4AbgCPAI8AvQC9ANwA3ADdAN0AxADEAKcApwCaAJoAowCjALIAsgC3ALcArACsAJ0AnQCdAJ0AuAC4AOEA4QD9AP0A9wD3AMoAygCPAI8AZABkAGEAYQCCAIIAsACwAM4AzgDOAM4AtgC2AJkAmQCJAIkAiQCJAI4AjgCLAIsAegB6AGUAZQBZAFkAYwBjAH8AfwCgAKAAtAC0ALEAsQCXAJcAcgByAE0ATQA0ADQALAAsADUANQBMAEwAaABoAH4AfgCCAIIAcABwAFAAUAAxADEAJQAlADQANABWAFYAegB6AIoAigB9AH0AWgBaADQANAAeAB4AJAAkAEIAQgBrAGsAjgCOAJ4AngCXAJcAfwB/AGQAZABTAFMAWQBZAHQAdACZAJkAtwC3ALwAvACnAKcAhgCGAG4AbgByAHIAkgCSALsAuwDUANQA0QDRALkAuQCiAKIAowCjAL4AvgDkAOQA+gD6APAA8ADNAM0ArQCtAKgAqADHAMcA+QD5AB0BHQEZARkB7wDvALUAtQCNAI0AjgCOALMAswDkAOQABAEEAQEBAQHeAN4AsQCxAJAAkACJAIkAmQCZALQAtADIAMgAzgDOAMUAxQCwALAAmQCZAIQAhABzAHMAZwBnAGEAYQBeAF4AWwBbAFMAUwBDAEMALQAtABYAFgAGAAYAAwADAAkACQAOAA4ABgAGAOv/6/+//7//kf+R/3D/cP9k/2T/av9q/3b/dv97/3v/cf9x/1z/XP9G/0b/OP84/zT/NP82/zb/M/8z/yj/KP8W/xb/B/8H/wP/A/8N/w3/HP8c/yT/JP8d/x3/BP8E/+j+6P7W/tb+2f7Z/vD+8P4O/w7/I/8j/yr/Kv8k/yT/IP8g/yj/KP87/zv/T/9P/1P/U/9D/0P/Kv8q/x//H/81/zX/bP9s/6//r//c/9z/3v/e/7f/t/+C/4L/Yv9i/2r/av+T/5P/xP/E/+H/4f/h/+H/0v/S/8v/y//Y/9j/8//z/wQABAD4//j/0//T/6v/q/+g/6D/wf/B/wIAAgBAAEAAWgBaAEMAQwANAA0A1v/W/7v/u//C/8L/3f/d//X/9f///////v/+/wEAAQARABEAKwArAD8APwA6ADoAFgAWAOH/4f+x/7H/nf+d/63/rf/V/9X/AQABAB0AHQAdAB0ABwAHAOf/5//K/8r/uP+4/7L/sv+0/7T/uf+5/77/vv/C/8L/xv/G/8j/yP/E/8T/t/+3/6H/of+I/4j/cv9y/2n/af9r/2v/dP90/3v/e/95/3n/bv9u/2L/Yv9Z/1n/VP9U/0//T/9F/0X/M/8z/yD/IP8U/xT/Ff8V/x//H/8q/yr/K/8r/yL/Iv8W/xb/E/8T/x7/Hv8w/zD/PP88/zj/OP8p/yn/Hf8d/yP/I/8//z//ZP9k/3z/fP96/3r/Yf9h/0b/Rv8+/z7/VP9U/37/fv+n/6f/uv+6/7X/tf+k/6T/mf+Z/6H/of+3/7f/0P/Q/+L/4v/o/+j/6P/o/+z/7P/3//f/BAAEAAwADAAMAAwABgAGAAAAAAABAAEACwALABwAHAAwADAAQgBCAE0ATQBPAE8ARwBHADsAOwAwADAAMAAwAD8APwBWAFYAbABsAHQAdABsAGwAWwBbAFAAUABWAFYAbQBtAIUAhQCOAI4AgACAAGQAZABOAE4ATgBOAGgAaACPAI8ArQCtALMAswChAKEAhwCHAHYAdgB2AHYAhACEAJEAkQCSAJIAiQCJAH4AfgB6AHoAggCCAI0AjQCSAJIAiQCJAHUAdQBfAF8AUQBRAE8ATwBTAFMAVgBWAFIAUgBIAEgAPAA8ADUANQAzADMAMwAzADMAMwAuAC4AJAAkABcAFwAIAAgA+P/4/+r/6v/j/+P/6f/p//r/+v8QABAAHQAdABcAFwD7//v/0v/S/7D/sP+j/6P/sP+w/8//z//t/+3//P/8//j/+P/m/+b/0//T/8n/yf/H/8f/yv/K/8v/y//K/8r/yP/I/8r/yv/P/8//1v/W/9r/2v/Y/9j/0v/S/83/zf/N/83/0//T/9r/2v/f/9//3v/e/9r/2v/V/9X/1//X/+H/4f/y//L/BQAFABAAEAAOAA4A/f/9/+P/4//N/83/yP/I/9v/2/8BAAEALgAuAE4ATgBVAFUAQgBCACQAJAALAAsABwAHABkAGQA3ADcAUgBSAGIAYgBkAGQAYQBhAGAAYABkAGQAawBrAGwAbABkAGQAVgBWAEoASgBKAEoAVwBXAG0AbQCDAIMAkACQAI8AjwCDAIMAcwBzAGQAZABaAFoAVwBXAFgAWABcAFwAYABgAGQAZABnAGcAaQBpAGwAbABtAG0AawBrAGMAYwBTAFMAPgA+ACsAKwAiACIAKgAqAEAAQABcAFwAcABwAHQAdABpAGkAVgBWAEUARQA+AD4AQQBBAEYARgBJAEkATQBNAFMAUwBgAGAAcABwAHsAewB3AHcAZABkAEkASQA1ADUAMgAyAEAAQABVAFUAZABkAGUAZQBcAFwAUwBTAFEAUQBZAFkAYwBjAGcAZwBiAGIAWABYAFAAUABSAFIAWwBbAGYAZgBsAGwAagBqAGEAYQBYAFgAVABUAFgAWABgAGAAawBrAHUAdQB5AHkAeAB4AHMAcwBrAGsAZgBmAGcAZwBsAGwAcwBzAHQAdABuAG4AYQBhAFUAVQBQAFAAVQBVAF4AXgBjAGMAXQBdAE8ATwBAAEAAOgA6AEEAQQBPAE8AWABYAFIAUgA8ADwAHwAfAAcABwD9//3/AgACABAAEAAbABsAHQAdABMAEwD/////5//n/9H/0f/A/8D/tv+2/7X/tf+5/7n/v/+//8D/wP+5/7n/qv+q/5b/lv+E/4T/dv92/3D/cP9w/3D/cv9y/3b/dv96/3r/e/97/3n/ef9w/3D/X/9f/0z/TP86/zr/Mv8y/zf/N/9F/0X/VP9U/13/Xf9Z/1n/S/9L/zz/PP81/zX/O/87/07/Tv9j/2P/cP9w/2//b/9g/2D/Tf9N/0L/Qv9H/0f/XP9c/3j/eP+O/47/lf+V/47/jv+C/4L/e/97/4H/gf+Q/5D/oP+g/6n/qf+q/6r/qP+o/6n/qf+x/7H/vv++/8j/yP/J/8n/xf/F/8T/xP/N/83/4//j//7//v8QABAAEAAQAP7//v/n/+f/2f/Z/+D/4P/6//r/HQAdADoAOgBGAEYAQgBCADUANQAoACgAIgAiACMAIwApACkAMgAyAD0APQBJAEkAUwBTAFoAWgBYAFgATQBNAD4APgAvAC8AKAAoACsAKwA1ADUAPgA+AEIAQgA+AD4ANAA0ACoAKgAjACMAHQAdABgAGAARABEACQAJAAMAAwACAAIABQAFAAoACgAJAAkA/////+3/7f/a/9r/zf/N/8j/yP/J/8n/y//L/8n/yf/D/8P/vP+8/7r/uv+7/7v/u/+7/7b/tv+s/6z/oP+g/5v/m/+f/5//qv+q/7P/s/+y/7L/pP+k/5H/kf+E/4T/g/+D/4z/jP+V/5X/lf+V/4n/if95/3n/bv9u/3H/cf+A/4D/kP+Q/5f/l/+Q/5D/gP+A/3H/cf9r/2v/bv9u/3f/d/99/33/f/9//3//f/+D/4P/i/+L/5b/lv+e/57/n/+f/5r/mv+U/5T/k/+T/5v/m/+o/6j/tf+1/77/vv/E/8T/yP/I/83/zf/U/9T/2v/a/93/3f/e/97/3//f/+T/5P/u/+7//P/8/wkACQARABEAFAAUABMAEwATABMAFgAWABwAHAAjACMAJwAnACcAJwAmACYAKQApADIAMgBAAEAATQBNAFIAUgBNAE0APwA/ADEAMQArACsAMAAwADwAPABJAEkAUABQAE8ATwBMAEwASgBKAE0ATQBSAFIAVABUAFAAUABHAEcAPwA/AD0APQBCAEIASgBKAE4ATgBKAEoAQABAADcANwA1ADUAOwA7AEIAQgBEAEQAOwA7ACsAKwAbABsAFAAUABsAGwApACkANgA2ADcANwAqACoAFAAUAAAAAAD3//f//f/9/wwADAAdAB0AJgAmACQAJAAaABoADQANAAQABAABAAEAAgACAAQABAAFAAUABwAHAAsACwASABIAGgAaAB0AHQAZABkADgAOAAIAAgD/////BwAHABsAGwAwADAAPgA+AEEAQQA6ADoAMAAwACkAKQApACkALAAsAC8ALwAyADIANAA0ADoAOgBCAEIASwBLAFAAUABPAE8ASABIAEAAQAA7ADsAOwA7AD8APwBDAEMARgBGAEgASABKAEoAUABQAFYAVgBcAFwAXABcAFcAVwBOAE4ARQBFAD8APwA/AD8ARQBFAE8ATwBYAFgAXgBeAF4AXgBXAFcATABMAEAAQAA3ADcAMwAzADQANAA3ADcAOQA5ADgAOAAzADMALgAuACgAKAAjACMAHAAcABUAFQAPAA8ACwALAAoACgALAAsADAAMAAoACgAFAAUA//////r/+v/5//n/+P/4//X/9f/u/+7/5f/l/97/3v/c/9z/4f/h/+r/6v/x//H/8v/y/+z/7P/h/+H/2f/Z/9j/2P/e/97/5v/m/+7/7v/y//L/8f/x//D/8P/v/+//8P/w//L/8v/y//L/7//v/+z/7P/t/+3/8v/y//z//P8HAAcADwAPABAAEAAMAAwABQAFAAAAAAABAAEABwAHAA4ADgATABMAFAAUABMAEwARABEAEgASABUAFQAbABsAHwAfACAAIAAfAB8AHQAdABwAHAAfAB8AJgAmAC8ALwA3ADcAPAA8AD8APwBBAEEARQBFAEkASQBOAE4ATwBPAE0ATQBIAEgARABEAEUARQBMAEwAVwBXAGAAYABhAGEAWgBaAE8ATwBFAEUAQgBCAEcARwBOAE4AUwBTAFIAUgBKAEoAQQBBADoAOgA1ADUAMwAzADAAMAArACsAJQAlACEAIQAeAB4AHAAcABkAGQASABIACQAJAAAAAAD5//n/9P/0//H/8f/u/+7/6v/q/+X/5f/g/+D/2v/a/9L/0v/H/8f/u/+7/6//r/+o/6j/p/+n/6v/q/+w/7D/sP+w/6r/qv+f/5//kv+S/4j/iP+D/4P/g/+D/4T/hP+G/4b/hf+F/4P/g/+C/4L/g/+D/4X/hf+H/4f/hv+G/4L/gv99/33/eP94/3j/eP9+/37/iP+I/5P/k/+a/5r/m/+b/5j/mP+W/5b/mP+Y/6D/oP+q/6r/sv+y/7X/tf+0/7T/tP+0/7r/uv/G/8b/1f/V/9//3//h/+H/2//b/9X/1f/V/9X/3f/d/+3/7f/7//v/BAAEAAUABQAEAAQABQAFAA0ADQAZABkAJAAkACkAKQAnACcAIwAjACMAIwAoACgAMwAzADwAPAA/AD8APAA8ADcANwA1ADUAOwA7AEQARABMAEwATQBNAEQARAA3ADcALQAtAC0ALQA1ADUAQQBBAEoASgBIAEgAPgA+ADAAMAAlACUAIQAhACMAIwAoACgAKQApACUAJQAdAB0AFQAVABAAEAAOAA4ADQANAAwADAAIAAgAAQABAPr/+v/0//T/8P/w/+3/7f/r/+v/6P/o/+X/5f/i/+L/4P/g/97/3v/c/9z/1//X/8//z//H/8f/v/+//7z/vP+8/7z/wP/A/8L/wv/B/8H/uv+6/7H/sf+o/6j/of+h/53/nf+d/53/nv+e/6H/of+j/6P/pv+m/6b/pv+j/6P/m/+b/5L/kv+L/4v/iv+K/5H/kf+e/57/rP+s/7P/s/+z/7P/rf+t/6b/pv+l/6X/qv+q/7P/s/+8/7z/wv/C/8T/xP/F/8X/yf/J/9D/0P/Z/9n/3//f/+D/4P/e/97/3P/c/9//3//n/+f/8//z//3//f8DAAMAAQABAPz//P/4//j/+v/6/wIAAgANAA0AFgAWABkAGQAVABUADgAOAAkACQAKAAoAEAAQABkAGQAgACAAJQAlACYAJgAmACYAJgAmACYAJgAkACQAIQAhAB8AHwAfAB8AIwAjACsAKwAzADMAOAA4ADYANgAwADAAKAAoACMAIwAjACMAJQAlACkAKQAsACwALQAtAC8ALwAwADAAMQAxADAAMAAsACwAJgAmACAAIAAdAB0AHQAdAB8AHwAhACEAIAAgAB0AHQAZABkAFwAXABcAFwAZABkAGgAaABgAGAATABMADAAMAAcABwAHAAcADAAMABQAFAAZABkAGgAaABUAFQANAA0ABQAFAAIAAgAFAAUACgAKAA8ADwASABIAEQARABAAEAARABEAFAAUABgAGAAZABkAFgAWAA4ADgAIAAgABwAHAA4ADgAaABoAJQAlACkAKQAlACUAHAAcABMAEwAOAA4AEAAQABcAFwAfAB8AJAAkACYAJgAmACYAJwAnACgAKAAqACoAKgAqACgAKAAkACQAIQAhACIAIgAnACcAMAAwADgAOAA8ADwAOwA7ADQANAArACsAJQAlACQAJAAnACcALAAsADAAMAAwADAALgAuACoAKgApACkAKgAqACwALAAsACwAKQApACMAIwAdAB0AGgAaABoAGgAdAB0AIQAhACMAIwAhACEAHgAeABkAGQAUABQAEQARAA8ADwAOAA4ADgAOAA0ADQAMAAwACgAKAAg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AtAC0ALoAugC/AL8AwwDDAMMAwwDDAMMAwgDCAMAAwAC+AL4AvAC8ALkAuQC2ALYAsgCyAK4ArgCqAKoApgCmAKQApACjAKMApACkAKQApACkAKQApACkAKMAowChAKEAoACgAJ8AnwCdAJ0AmwCbAJgAmACWAJYAlQCVAJUAlQCUAJQAkwCTAJAAkACMAIwAhwCHAIIAggB8AHwAdwB3AG4AbgBiAGIAVABUAEMAQwAyADIAIwAjABYAFgAIAAgA+v/6/+n/6f/V/9X/wP/A/6z/rP+Z/5n/h/+H/3X/df9j/2P/UP9Q/zz/PP8p/yn/GP8Y/wr/Cv///v/+9P70/ur+6v7h/uH+1/7X/s/+z/7I/sj+wv7C/rz+vP62/rb+rv6u/qf+p/6g/qD+m/6b/pj+mP6W/pb+lP6U/pL+kv6Q/pD+jf6N/or+iv6I/oj+hf6F/oH+gf57/nv+c/5z/mv+a/5k/mT+Xv5e/lv+W/5Y/lj+Vf5V/lP+U/5R/lH+T/5P/k7+Tv5P/k/+UP5Q/lL+Uv5U/lT+Vf5V/lb+Vv5Z/ln+W/5b/mD+YP5m/mb+bf5t/nX+df59/n3+hf6F/o7+jv6W/pb+nv6e/qb+pv6s/qz+sP6w/rT+tP62/rb+uP64/rv+u/7A/sD+xP7E/sn+yf7N/s3+0f7R/tb+1v7c/tz+4v7i/un+6f7v/u/+8/7z/vf+9/76/vr+/P78/gD/AP8G/wb/Dv8O/xb/Fv8g/yD/Kv8q/zX/Nf9B/0H/TP9M/1j/WP9k/2T/cP9w/33/ff+K/4r/mf+Z/6r/qv+8/7z/zv/O/+D/4P/y//L/AwADABYAFgAoACgAPAA8AE4ATgBfAF8AbwBvAHwAfACHAIcAkgCSAJ0AnQCoAKgAtQC1AMIAwgDQANAA3gDeAOwA7AD6APoACQEJARkBGQEpASkBOgE6AUoBSgFZAVkBZwFnAXUBdQGDAYMBlQGVAakBqQG/Ab8B1wHXAe0B7QEEAgQCHAIcAjMCMwJKAkoCYwJjAnkCeQKNAo0CoQKhArICsgLDAsMC1gLWAugC6AL6AvoCDAMMAxsDGwMoAygDNAM0Az8DPwNJA0kDVQNVA10DXQNiA2IDZgNmA2cDZwNmA2YDaANoA2kDaQNpA2kDaQNpA2YDZgNhA2EDXgNeA1sDWwNZA1kDWANYA1UDVQNNA00DRQNFAzoDOgMwAzADKgMqAyQDJAMdAx0DFQMVAwkDCQP8AvwC8QLxAuYC5gLcAtwC0gLSAsUCxQK0ArQCogKiAo0CjQJ3AncCYgJiAkwCTAIzAjMCGQIZAvwB/AHdAd0BvgG+AZ0BnQF6AXoBVgFWATEBMQENAQ0B6wDrAMoAygCpAKkAiACIAGYAZgBDAEMAIQAhAAEAAQDk/+T/yf/J/7H/sf+Z/5n/gP+A/2j/aP9Q/1D/OP84/yH/If8M/wz/9/73/uP+4/7O/s7+uv66/qb+pv6T/pP+gv6C/nL+cv5k/mT+Vf5V/kf+R/45/jn+Kf4p/hr+Gv4L/gv+/P38/e797v3g/eD90v3S/cX9xf24/bj9q/2r/Z/9n/2T/ZP9h/2H/X39ff11/XX9bv1u/Wn9af1k/WT9X/1f/Vv9W/1Z/Vn9WP1Y/Vr9Wv1f/V/9ZP1k/Wv9a/10/XT9ff19/Yv9i/2d/Z39sv2y/cv9y/3l/eX9/v3+/Rf+F/4x/jH+TP5M/mj+aP6G/ob+ov6i/r3+vf7V/tX+6/7r/v/+//4T/xP/Jf8l/zb/Nv9E/0T/UP9Q/1r/Wv9j/2P/bf9t/3b/dv9//3//h/+H/47/jv+W/5b/n/+f/6n/qf+1/7X/wf/B/8z/zP/V/9X/3v/e/+j/6P/1//X/BAAEABMAEwAjACMAMQAxAD4APgBKAEoAWABYAGgAaAB4AHgAiACIAJcAlwCnAKcAtwC3AMgAyADZANkA7ADsAP0A/QAOAQ4BHwEfAS4BLgE9AT0BSgFKAVQBVAFbAVsBXwFfAV8BXwFeAV4BWwFbAVcBVwFPAU8BRgFGATsBOwEuAS4BIQEhARQBFAEIAQgB/AD8AO8A7wDhAOEA1ADUAMgAyAC9AL0AtAC0AKsAqwChAKEAlgCWAIoAigB8AHwAbgBuAGAAYABRAFEAQABAAC0ALQAYABgAAgACAOz/7P/W/9b/vv++/6X/pf+K/4r/bf9t/1L/Uv83/zf/Hf8d/wT/BP/q/ur+zv7O/rL+sv6Y/pj+f/5//mv+a/5Z/ln+R/5H/jb+Nv4m/ib+F/4X/gz+DP4F/gX+AP4A/vv9+/31/fX97f3t/eX95f3e/d792P3Y/dT91P3O/c79xf3F/br9uv2u/a79oP2g/ZT9lP2J/Yn9fP18/W79bv1d/V39R/1H/TH9Mf0Z/Rn9/fz9/OD84Py//L/8lvyW/Gr8avw6/Dr8BPwE/M77zvuW+5b7WftZ+xv7G/vd+t36nfqd+mP6Y/ot+i36+fn5+c75zvmn+af5g/mD+Wn5aflX+Vf5TPlM+Uz5TPlV+VX5Y/lj+Xj5ePmS+ZL5rfmt+c/5z/n2+fb5Hvoe+kr6Svp4+nj6pfql+tP60/oB+wH7LPss+1r7WvuJ+4n7ufu5++z77Psi/CL8V/xX/Iv8i/y+/L787Pzs/Bn9Gf1E/UT9bP1s/ZH9kf20/bT90v3S/e397f0G/gb+Hv4e/jn+Of5Y/lj+ev56/qH+of7K/sr+9/73/ij/KP9d/13/mP+Y/9j/2P8fAB8AbQBtAMAAwAAZARkBeAF4AdoB2gFCAkICrgKuAhsDGwOKA4oD+wP7A2sEawTZBNkESAVIBbAFsAUVBhUGegZ6BtoG2gY3BzcHlAeUB+sH6wc+CD4IjwiPCNgI2AgbCRsJXQldCZYJlgnICcgJ9wn3CRoKGgo1CjUKTgpOCl0KXQpmCmYKbgpuCm0KbQpnCmcKYApgClMKUwpDCkMKNQo1CiEKIQoICggK8QnxCdMJ0wmzCbMJlwmXCXcJdwlVCVUJNgk2CREJEQnqCOoIxAjECJoImghuCG4IQwhDCBEIEQjcB9wHpwenB24HbgczBzMH+wb7BsAGwAaDBoMGSAZIBgsGCwbOBc4FkwWTBVYFVgUaBRoF3wTfBKMEowRmBGYEKgQqBO0D7QOvA68DcwNzAzYDNgP6AvoCwALAAoYChgJMAkwCFAIUAtsB2wGkAaQBbwFvATsBOwEIAQgB1gDWAKQApABxAHEAPgA+AAsACwDZ/9n/pf+l/3L/cv8+/z7/Cf8J/9T+1P6h/qH+b/5v/j/+P/4Q/hD+4f3h/bP9s/2G/Yb9WP1Y/S39Lf0D/QP92vza/LP8s/yP/I/8a/xr/Ev8S/ww/DD8F/wX/AT8BPz3+/f77Pvs++f75/vm++b76fvp+/H78fv8+/z7CPwI/Bf8F/wn/Cf8N/w3/Er8Svxh/GH8efx5/JX8lfyx/LH8y/zL/OT85Pz9/P38E/0T/Sf9J/05/Tn9Rv1G/U79Tv1T/VP9U/1T/VL9Uv1Q/VD9S/1L/Uf9R/1C/UL9O/07/Tb9Nv0x/TH9K/0r/ST9JP0d/R39E/0T/Qz9DP0G/Qb9Af0B/f78/vz8/Pz8+fz5/Pj8+Pz4/Pj8+fz5/Pz8/PwC/QL9Bf0F/Qr9Cv0P/Q/9Ev0S/Rj9GP0e/R79JP0k/Sz9LP01/TX9Pv0+/Un9Sf1V/VX9YP1g/Wv9a/12/Xb9fv1+/Yf9h/2P/Y/9lf2V/Zv9m/2e/Z79n/2f/aD9oP2h/aH9ov2i/aX9pf2o/aj9qv2q/a79rv2z/bP9t/23/b79vv3G/cb9zv3O/dn92f3m/eb99v32/Qr+Cv4i/iL+PP48/lj+WP51/nX+kv6S/q/+r/7M/sz+6P7o/gP/A/8d/x3/Nf81/03/Tf9m/2b/gP+A/5r/mv+0/7T/zP/M/+P/4//2//b/BwAHABYAFgAjACMALwAvADkAOQBBAEEASABIAE0ATQBRAFEAVABUAFcAVwBZAFkAWwBbAFoAWgBYAFgAVgBWAFUAVQBWAFYAWgBaAGAAYABoAGgAcgByAH4AfgCMAIwAnQCdAK8ArwDDAMMA1gDWAOcA5wD3APcABwEHARYBFgEjASMBMQExATwBPAFGAUYBUAFQAVoBWgFmAWYBcgFyAX4BfgGHAYcBjQGNAY4BjgGMAYwBiAGIAYABgAF2AXYBaAFoAVcBVwFEAUQBMAEwARsBGwEGAQYB8QDxANsA2wDEAMQArACsAJQAlAB8AHwAZABkAEwATAA0ADQAHwAfAAsACwD6//r/6v/q/9v/2//M/8z/vf+9/67/rv+f/5//kP+Q/4H/gf9x/3H/YP9g/1H/Uf9D/0P/OP84/zL/Mv8v/y//L/8v/zL/Mv83/zf/P/8//0j/SP9T/1P/Xv9e/2n/af90/3T/fv9+/4j/iP+R/5H/mP+Y/57/nv+j/6P/pf+l/6b/pv+m/6b/o/+j/53/nf+W/5b/i/+L/3//f/9w/3D/X/9f/0v/S/8y/zL/Fv8W//b+9v7R/tH+p/6n/nf+d/5A/kD+//3//bj9uP1q/Wr9Ff0V/bz8vPxg/GD8//v/+6H7oftG+0b77/rv+qP6o/pg+mD6JPok+vT59PnL+cv5qPmo+Y/5j/l/+X/5c/lz+XL5cvl3+Xf5fvl++Y35jfmh+aH5tvm2+dP50/nz+fP5FvoW+j36Pfpp+mn6lfqV+sb6xvr7+vv6Mfsx+2v7a/uq+6r76Pvo+yr8Kvxr/Gv8qfyp/OT85Pwa/Rr9SP1I/XD9cP2S/ZL9q/2r/b/9v/3P/c/92/3b/eT95P3t/e399f31/f/9//0M/gz+Hv4e/jX+Nf5U/lT+d/53/p/+n/7M/sz+/v7+/jX/Nf9z/3P/uf+5/wYABgBaAFoAtgC2ABcBFwF/AX8B7AHsAVsCWwLNAs0CQgNCA7UDtQMqBCoEowSjBBkFGQWOBY4FBAYEBnQGdAbgBuAGTAdMB7EHsQcTCBMIdQh1CMwIzAgbCRsJZQllCaMJownYCdgJCwoLCjQKNApVClUKdAp0CogKiAqVCpUKoQqhCqQKpAqjCqMKogqiCpkKmQqKCooKewp7CmQKZApICkgKLQotCgsKCwrmCeYJwgnCCZcJlwloCWgJOgk6CQcJBwnSCNIIngieCGcIZwguCC4I+Af4B74HvgeCB4IHRwdHBwgHCAfHBscGhgaGBkIGQgb8BfwFtwW3BXAFcAUnBScF4QThBJwEnARXBFcEFQQVBNMD0wOSA5IDVANUAxgDGAPgAuACrgKuAoACgAJWAlYCMAIwAg0CDQLrAesBywHLAa0BrQGPAY8BcQFxAVEBUQEtAS0BBwEHAd0A3QCyALIAhQCFAFgAWAApACkA+f/5/8j/yP+X/5f/Zv9m/zj/OP8M/wz/4f7h/rf+t/6N/o3+Y/5j/jv+O/4V/hX+7/3v/cz9zP2o/aj9g/2D/V39Xf04/Tj9Ev0S/e/87/zO/M78rfyt/JD8kPx0/HT8WfxZ/EP8Q/wx/DH8Ivwi/Bv8G/wZ/Bn8HPwc/Cf8J/w5/Dn8T/xP/Gz8bPyN/I38r/yv/NP80/z3/Pf8GP0Y/Tj9OP1U/VT9bf1t/YH9gf2S/ZL9n/2f/an9qf2y/bL9uf25/cH9wf3J/cn90v3S/dz93P3n/ef98f3x/fz9/P0H/gf+EP4Q/hr+Gv4k/iT+LP4s/jP+M/43/jf+Nv42/jL+Mv4r/iv+IP4g/hT+FP4G/gb+9f31/eH94f3L/cv9sv2y/Zr9mv2F/YX9dP10/Wf9Z/1d/V39VP1U/Uz9TP1G/Ub9Qv1C/UL9Qv1E/UT9Rv1G/Uf9R/1H/Uf9RP1E/UL9Qv1E/UT9SP1I/U/9T/1W/Vb9Xf1d/WT9ZP1s/Wz9df11/YL9gv2R/ZH9oP2g/bD9sP3B/cH91P3U/ez97P0K/gr+LP4s/lD+UP52/nb+mf6Z/rz+vP7e/t7+/f79/hv/G/81/zX/Sf9J/1n/Wf9k/2T/bP9s/3P/c/95/3n/fP98/3z/fP95/3n/cv9y/2n/af9f/1//Vv9W/0z/TP9B/0H/Nf81/yb/Jv8X/xf/B/8H//j++P7r/uv+3v7e/tT+1P7N/s3+x/7H/sX+xf7G/sb+yf7J/s7+zv7W/tb+3/7f/uz+7P79/v3+EP8Q/yb/Jv88/zz/Uv9S/2j/aP9//3//lv+W/67/rv/G/8b/3//f//b/9v8NAA0AIwAjADgAOABOAE4AYwBjAHcAdwCKAIoAnACcAKsAqwC3ALcAwgDCAMkAyQDPAM8A0gDSANMA0wDTANMA0QDRAM0AzQDIAMgAwADAALcAtwCrAKsAnQCdAI8AjwCAAIAAcgByAGQAZABVAFUARQBFADQANAAjACMAEgASAAQABAD2//b/5//n/9f/1//C/8L/q/+r/5H/kf92/3b/W/9b/0D/QP8m/yb/DP8M//X+9f7h/uH+0f7R/sb+xv7A/sD+vv6+/sH+wf7H/sf+0P7Q/tv+2/7m/ub+8v7y/v7+/v4L/wv/Gf8Z/yj/KP86/zr/Tf9N/2L/Yv96/3r/k/+T/63/rf/H/8f/3//f//T/9P8GAAYAFAAUACAAIAAnACcAKgAqACgAKAAgACAAEAAQAPr/+v/a/9r/r/+v/3n/ef80/zT/3/7f/n/+f/4R/hH+mP2Y/Rj9GP2S/JL8CfwJ/IX7hfsH+wf7j/qP+iT6JPrE+cT5bflt+ST5JPno+Oj4sviy+In4ifhp+Gn4TPhM+Dn4Ofgu+C74J/gn+Cr4Kvg0+DT4QvhC+Fv4W/h++H74qfip+OD44Pgi+SL5Z/ln+bT5tPkG+gb6Wvpa+rT6tPoQ+xD7aftp+777vvsM/Az8T/xP/Iz8jPzC/ML87/zv/Bb9Fv01/TX9S/1L/V39Xf1r/Wv9eP14/Yf9h/2Y/Zj9qf2p/bz9vP3R/dH96P3o/QP+A/4j/iP+Rv5G/m3+bf6Y/pj+yP7I/v7+/v49/z3/hP+E/9T/1P8uAC4AjwCPAPYA9gBkAWQB2AHYAVACUALNAs0CUgNSA9cD1wNfBF8E6QTpBHEFcQX4BfgFggaCBgcHBweKB4oHDAgMCIQIhAj0CPQIYQlhCcMJwwkdCh0Kdgp2CsUKxQoMCwwLUwtTC48LjwvFC8UL+wv7CycMJwxNDE0McwxzDI4MjgyhDKEMtAy0DLoMugy5DLkMtgy2DKcMpwyRDJEMeQx5DFcMVwwvDC8MBwwHDNcL1wuhC6ELbQttCzELMQvwCvAKrwqvCmUKZQoVChUKwwnDCWgJaAkHCQcJpAikCDwIPAjPB88HZAdkB/cG9waJBokGIAYgBrgFuAVTBVMF8wTzBJUElQQ5BDkE4gPiA48DjwNBA0ED+wL7ArkCuQJ9An0CRwJHAhcCFwLtAe0BywHLAa4BrgGUAZQBfQF9AWcBZwFQAVABOwE7AScBJwEYARgBCwELAQEBAQH3APcA7gDuAOUA5QDcANwA0gDSAMgAyAC6ALoAqACoAJAAkAByAHIATwBPACgAKAD9//3/0P/Q/6H/of9w/3D/Pf89/wj/CP/R/tH+mP6Y/l3+Xf4g/iD+4f3h/aH9of1h/WH9IP0g/eP84/yq/Kr8dPx0/ET8RPwZ/Bn88fvx+8/7z/uy+7L7mPuY+4T7hPt1+3X7Zvtm+1v7W/tS+1L7S/tL+0f7R/tI+0j7SvtK+1L7Uvtc+1z7aPto+3f7d/uK+4r7n/uf+7n7ufvY+9j7+vv6+x/8H/xI/Ej8cfxx/J38nfzL/Mv8+Pz4/Cf9J/1V/VX9gP2A/an9qf3Q/dD98/3z/RX+Ff42/jb+Vf5V/nL+cv6L/ov+nv6e/q3+rf66/rr+xP7E/s7+zv7W/tb+2f7Z/tn+2f7U/tT+y/7L/sD+wP60/rT+p/6n/pr+mv6L/ov+ef55/mj+aP5Z/ln+S/5L/kH+Qf47/jv+N/43/jb+Nv42/jb+Nv42/jf+N/43/jf+Nf41/jL+Mv4v/i/+K/4r/ir+Kv4q/ir+Lf4t/jP+M/49/j3+Sv5K/lv+W/5w/nD+h/6H/qD+oP66/rr+0f7R/ub+5v72/vb+Af8B/wf/B/8L/wv/DP8M/wz/DP8K/wr/Bv8G//7+/v7z/vP+4/7j/tL+0v6//r/+qf6p/pP+k/59/n3+Zf5l/k/+T/47/jv+Kf4p/h3+Hf4U/hT+D/4P/g7+Dv4P/g/+Ef4R/hT+FP4X/hf+GP4Y/hr+Gv4e/h7+Jv4m/jL+Mv5F/kX+W/5b/nX+df6Q/pD+qv6q/sX+xf7g/uD++f75/hD/EP8l/yX/Nf81/0L/Qv9K/0r/Tv9O/07/Tv9K/0r/RP9E/zv/O/8w/zD/I/8j/xT/FP8E/wT/8/7z/uH+4f7Q/tD+wP7A/rT+tP6t/q3+qP6o/qf+p/6o/qj+qP6o/qf+p/6n/qf+pv6m/qf+p/6o/qj+qf6p/qn+qf6p/qn+qP6o/qn+qf6t/q3+tf61/sH+wf7P/s/+2/7b/uT+5P7q/ur+6/7r/un+6f7i/uL+1/7X/sr+yv65/rn+pv6m/pT+lP6F/oX+ev56/nT+dP5z/nP+df51/nr+ev5//n/+hf6F/ov+i/6R/pH+l/6X/p3+nf6j/qP+q/6r/rf+t/7K/sr+4/7j/gX/Bf8t/y3/WP9Y/4b/hv+z/7P/3//f/wkACQAwADAAVQBVAHUAdQCTAJMArQCtAMUAxQDbANsA8ADwAAABAAEHAQcB/wD/AOIA4gCqAKoAUQBRANf/1/89/z3/g/6D/rP9s/3Q/ND84Pvg+/L68voM+gz6MPkw+Wz4bPi897z3HPcc94/2j/YM9gz2jPWM9RX1FfWg9KD0JvQm9LDzsPM78zvzxfLF8lzyXPIG8gbywfHB8Z7xnvGb8ZvxsvGy8evx6/E+8j7ypPKk8iHzIfO087TzVPRU9Af1B/XJ9cn1lPaU9mv3a/dL+Ev4Lvku+Rf6F/oF+wX79Pv0++P84/zP/c/9tv62/pf/l/91AHUAVAFUATECMQISAxID+AP4A9wE3ATBBcEFpwanBoYHhgdfCF8INQk1CfoJ+gmxCrEKXgteC/gL+AuHDIcMEg0SDY8Njw0FDgUOew57DuQO5A5GD0YPpw+nD/sP+w9FEEUQixCLEL8QvxDkEOQQAREBEQgRCBEAEQAR8hDyENQQ1BCpEKkQexB7ED0QPRDzD/MPpA+kD0YPRg/eDt4Odg52DgMOAw6LDYsNFA0UDZcMlwwYDBgMoguiCy0LLQu+Cr4KWQpZCvYJ9gmXCZcJPgk+CeQI5AiLCIsINQg1CNwH3AeBB4EHKQcpB9EG0QZ8BnwGLwYvBugF6AWlBaUFaQVpBS4FLgXzBPMEuAS4BHcEdwQsBCwE1wPXA3QDdAMEAwQDiAKIAgQCBAJ6AXoB8ADwAGcAZwDg/+D/W/9b/9r+2v5a/lr+2P3Y/Vf9V/3S/NL8SPxI/L77vvsy+zL7pfql+iD6IPqk+aT5M/kz+df41/iP+I/4WfhZ+Dn4Ofgr+Cv4Kfgp+Db4NvhN+E34afhp+JD4kPjC+ML4/vj++Er5Svmp+an5GPoY+pn6mfop+yn7xfvF+2r8avwY/Rj9yP3I/Xj+eP4n/yf/0P/Q/3QAdAAVARUBtQG1AVMCUwLzAvMClQOVAzMEMwTOBM4EYwVjBeoF6gViBmIGywbLBh0HHQdbB1sHiQeJB6MHowesB6wHqgeqB5kHmQd+B34HXQddBzMHMwcCBwIHzAbMBo4GjgZGBkYG9gX2BZwFnAU4BTgFzQTNBFkEWQTcA9wDWwNbA9MC0wJGAkYCtAG0ASABIAGJAIkA8//z/2H/Yf/V/tX+Uv5S/tr92v1s/Wz9B/0H/a38rfxa/Fr8DfwN/Mj7yPuI+4j7SftJ+xD7EPvY+tj6ofqh+m/6b/pB+kH6FvoW+vP58/nZ+dn5xPnE+bn5ufm2+bb5tvm2+bz5vPnF+cX5zfnN+dr52vno+ej59vn2+QX6BfoV+hX6I/oj+jT6NPpJ+kn6Xvpe+nr6evqY+pj6tfq1+tX61fr5+vn6H/sf+0v7S/t++377svuy++v76/sn/Cf8ZPxk/KX8pfzo/Oj8Kv0q/Wv9a/2o/aj93/3f/RD+EP4+/j7+av5q/pT+lP6+/r7+5/7n/g//D/83/zf/Xv9e/4X/hf+s/6z/0f/R//P/8/8QABAAJwAnADkAOQBHAEcATwBPAFMAUwBSAFIASgBKADsAOwAlACUACQAJAOr/6v/J/8n/p/+n/4X/hf9j/2P/Qf9B/yH/If8F/wX/7f7t/tn+2f7I/sj+t/63/qX+pf6Q/pD+ev56/mP+Y/5M/kz+Nv42/iH+If4N/g3++/37/ez97P3i/eL93P3c/d393f3i/eL96v3q/fT99P3//f/9C/4L/hj+GP4l/iX+Mv4y/j3+Pf5E/kT+Rv5G/kX+Rf5A/kD+PP48/jr+Ov49/j3+Q/5D/k/+T/5h/mH+eP54/pb+lv66/rr+4P7g/gb/Bv8o/yj/Q/9D/1b/Vv9j/2P/af9p/2r/av9n/2f/YP9g/1b/Vv9L/0v/P/8//zH/Mf8f/x//B/8H/+j+6P7A/sD+jv6O/lT+VP4R/hH+xf3F/XP9c/0c/Rz9wPzA/Gj8aPwT/BP8wvvC+3v7e/s8+zz7AvsC+9P60/qv+q/6lfqV+or6ivqQ+pD6o/qj+sj6yPr9+v36P/s/+5D7kPvt++37VfxV/Mb8xvxB/UH9wP3A/UT+RP7N/s3+W/9b/+3/7f+EAIQAIAEgAb0BvQFYAlgC8ALwAn4DfgMDBAMEfwR/BOsE6wRHBUcFkwWTBckFyQXoBegF7wXvBdUF1QWVBZUFKwUrBY8EjwS6A7oDrQKtAmkBaQHv/+//Rv5G/n/8f/yi+qL6u/i7+OL24vYb9Rv1Z/Nn89bx1vFf8F/w8+7z7qHtoe1Z7FnsDusO69Pp0+mi6KLodOd052rmauaG5Yblx+TH5E3kTeQZ5BnkIuQi5H3kfeQj5SPlA+YD5iXnJeeB6IHoBuoG6rvru+ug7aDtrO+s7+Lx4vFD9EP0xfbF9mL5Yvka/Br84/7j/q8BrwF9BH0ERgdGB/YJ9gmTDJMMIA8gD4wRjBHlE+UTNBY0FmIYYhh4GngagRyBHFseWx4NIA0gnSGdIeUi5SLoI+gjsSSxJB8lHyVBJUElLSUtJdAk0CQ9JD0kjyOPI7UitSK8IbwhuyC7IJ8fnx9uHm4eOR05Hesb6xuIGogaHBkcGZkXmRcFFgUWbxRvFNIS0hI1ETURpw+nDyIOIg6rDKsMSAtIC/UJ9QmzCLMIhweHB2sGawZhBWEFagRqBIUDhQOyArIC8gHyAUcBRwGuAK4AJAAkAKj/qP80/zT/wv7C/lH+Uf7c/dz9YP1g/d383fxS/FL8vfu9+yT7JPuH+of65/nn+Ur5Svmw+LD4FPgU+H73fvfr9uv2U/ZT9sH1wfUv9S/1mPSY9Aj0CPR683rz6/Lr8mryavL18fXxjfGN8UDxQPES8RLx/vD+8BXxFfFW8VbxvPG88U7yTvIK8wrz5/Pn8+b05vQF9gX2Pvc+95D4kPj8+fz5fPt8+w39Df2u/q7+WwBbAAsCCwLAA8ADdgV2BR0HHQe1CLUIOwo7Cp4LngvgDOAMBg4GDgIPAg/ZD9kPkRCREBwRHBF8EXwRuBG4EcERwRGaEZoRRxFHEb4QvhABEAEQHA8cDwoOCg7UDNQMhwuHCyYKJgq4CLgISAdIB9kF2QVtBG0EBwMHA6sBqwFVAFUACP8I/8j9yP2V/JX8cftx+2j6aPp8+Xz5sfix+BD4EPia95r3SvdK9yX3Jfcj9yP3Pfc993P3c/e/97/3G/gb+In4ifgG+Qb5jPmM+SD6IPrA+sD6Z/tn+xP8E/y//L/8Yv1i/fX99f1w/nD+yv7K/gD/AP8O/w7/8v7y/rL+sv5T/lP+3f3d/Vz9XP3X/Nf8VfxV/N373ftu+277BvsG+6b6pvpJ+kn66Pno+Yb5hvkf+R/5sfix+Ef4R/ji9+L3h/eH90P3Q/ca9xr3DvcO9yn3Kfds92z30vfS9174XvgM+Qz51PnU+bL6svqj+6P7ofyh/Kf9p/22/rb+yv/K/9sA2wDpAekB7gLuAuED4QPABMAEigWKBTcGNwbLBssGSgdKB60HrQf6B/oHNAg0CFQIVAhdCF0IUghSCCwILAjpB+kHigeKBwkHCQdjBmMGnQWdBbYEtgSwA7ADkwKTAmUBZQEqACoA7P7s/rf9t/2Q/JD8e/t7+4L6gvql+aX54fjh+D74Pvi197X3Q/dD9+v26/ao9qj2c/Zz9lP2U/ZE9kT2Q/ZD9ln2WfaF9oX2wfbB9hL3Evd193X35ffl92L4Yvjq+Or4dvl2+QX6BfqX+pf6Jfsl+7L7svs+/D78xvzG/En9Sf3F/cX9Nf41/pj+mP7s/uz+Lv8u/13/Xf96/3r/hv+G/4P/g/94/3j/af9p/17/Xv9a/1r/X/9f/27/bv+F/4X/oP+g/73/vf/W/9b/6v/q//X/9f/5//n/9//3//T/9P/0//T//v/+/xQAFAA5ADkAbwBvALMAswAGAQYBZgFmAc8BzwE+Aj4CswKzAiYDJgOYA5gDCAQIBHEEcQTTBNMELQUtBXcFdwWsBawFywXLBc4FzgW1BbUFgwWDBTcFNwXVBNUEYQRhBN0D3QNMA0wDsQKxAg8CDwJmAWYBtQC1APz//P83/zf/Y/5j/oX9hf2d/J38qvuq+7r6uvrO+c756Pjo+Bf4F/hb91v3tPa09i72LvbG9cb1e/V79VT1VPVQ9VD1avVq9an1qfUM9gz2i/aL9iv3K/fp9+n3wPjA+K/5r/mz+rP6x/vH++b85vwP/g/+Pf89/2sAawCaAZoBxwLHAuoD6gMEBQQFEwYTBgwHDAfuB+4HuAi4CF8JXwnfCd8JOQo5CmQKZApdCl0KKQopCsMJwwkoCSgJWAhYCEwHTAf4BfgFWgRaBHMCcwI9AD0Av/2//Q37Dfst+C34LfUt9TTyNPJN703vhuyG7AjqCOrS59Ln3OXc5TzkPOTb4tvio+Gj4aLgouDJ38nfBd8F33Xedd4Y3hje6t3q3RLeEt6Y3pjed99338vgy+CO4o7iq+Sr5CLnIufj6ePp0uzS7Ofv5+8c8xzzZvZm9r75vvkr/Sv9qwCrAC8ELwS6B7oHRgtGC7QOtA4AEgASIBUgFe0X7RdqGmoanhyeHG8ebx7zH/MfQyFDIU4iTiIpIykj6yPrI3kkeSTbJNskHiUeJR0lHSXaJNokYCRgJJMjkyN/In8iPyE/Icsfyx87HjserxyvHCIbIhulGaUZSxhLGAQXBBfTFdMVvRS9FLATsBOsEqwSuBG4EcYQxhDbD9sPAQ8BDy4OLg5mDWYNsQyxDAMMAwxaC1oLtQq1CgYKBgpGCUYJcwhzCIkHiQeJBokGeQV5BV0EXQQ4AzgDDwIPAuQA5ACy/7L/ef55/jn9Of3q++r7hvqG+hP5E/mM94z38PXw9VL0UvS08rTyG/Eb8aDvoO9H7kfuD+0P7RDsEOxG60brrOqs6lLqUuow6jDqO+o76n/qf+r26vbqlOuU62XsZexp7WntmO6Y7vjv+O+K8YrxQvNC8yH1IfUl9yX3RPlE+Xj7ePvA/cD9FgAWAGsCawLBBMEEFAcUB1QJVAmCC4ILnA2cDYsPiw9REVER7BLsEkUURRRcFVwVNhY2FsIWwhYDFwMXCBcIF8QWxBZBFkEWjxWPFaQUpBSJE4kTSxJLEuYQ5hBgD2APxA3EDREMEQxJCkkKdQh1CJoGmga6BLoE3wLfAhIBEgFV/1X/rf2t/Sb8JvzA+sD6fvl++Wz4bPiJ94n30fbR9kv2S/bx9fH1u/W79a/1r/XJ9cn1AvYC9l32XfbV9tX2YPdg9wH4Afix+LH4a/lr+Sv6K/ru+u76qfup+1r8Wvz8/Pz8if2J/QT+BP5s/mz+w/7D/g3/Df9M/0z/ff99/6L/ov+z/7P/rf+t/4n/if9B/0H/z/7P/jX+Nf51/XX9lfyV/KX7pfu0+rT60fnR+RH5Efl/+H/4Ifgh+P33/fcN+A34RPhE+Jv4m/gB+QH5avlq+dH50fkz+jP6kfqR+vT69Ppk+2T75/vn+4b8hvxF/UX9I/4j/hv/G/8lACUAOAE4AUMCQwI8AzwDHgQeBN8E3wR+BX4FAQYBBmQGZAasBqwG3QbdBvIG8gbuBu4G0AbQBpMGkwY1BjUGuAW4BRsFGwVgBGAEjgOOA6wCrALAAcAB0ADQAOP/4//6/vr+Fv4W/jz9PP1n/Gf8lfuV+8z6zPoH+gf6RPlE+Yz4jPji9+L3RfdF98X2xfZl9mX2JfYl9g72DvYe9h72TPZM9pv2m/YC9wL3dfd19/T39Pd2+Hb48Pjw+GP5Y/nL+cv5JPok+nT6dPq8+rz6+vr6+jb7Nvtw+3D7pful+9r72vsM/Az8Ofw5/GP8Y/yG/Ib8ofyh/Lb8tvzG/Mb80PzQ/Nj82Pzc/Nz83Pzc/Nn82fzU/NT8yvzK/L/8v/y1/LX8rfyt/Kz8rPy5/Ln82PzY/Az9DP1Z/Vn9v/2//Tr+Ov7J/sn+Zv9m/wsACwCxALEAUgFSAeUB5QFqAmoC3wLfAkMDQwOaA5oD5wPnAykEKQRiBGIElQSVBMAEwATjBOMEAQUBBRMFEwUaBRoFFQUVBQIFAgXhBOEEtwS3BIMEgwRFBEUE/wP/A60DrQNPA08D5QLlAnACcALwAfABaQFpAd0A3QBQAFAAxv/G/0X/Rf/Q/tD+aP5o/hP+E/7O/c79lv2W/Wv9a/1H/Uf9Iv0i/fr8+vzJ/Mn8i/yL/EP8Q/zy+/L7nPuc+0r7SvsE+wT7zfrN+q/6r/qs+qz6wfrB+u767vot+y37dvt2+8T7xPsU/BT8X/xf/Kf8p/zr/Ov8Lf0t/XD9cP29/b39Fv4W/oH+gf4B/wH/lf+V/zoAOgDuAO4ArQGtAW8CbwIyAzID8gPyA6cEpwRQBVAF7AXsBXUGdQbrBusGUQdRB6MHowfhB+EHEggSCC8ILwg7CDsINwg3CBwIHAjeB94HeAd4B9oG2gbxBfEFswSzBBkDGQMXARcBsP6w/vj7+Pv6+Pr4zfXN9aDyoPKI74jvmOyY7Pjp+Omn56fnl+WX5drj2uNU4lTi5+Dn4KHfod9v3m/eQN1A3TfcN9xZ21nbp9qn2lHaUdpj2mPa29rb2trb2tta3VrdQt9C35bhluFD5EPkLect50/qT+qk7aTtIfEh8cP0w/SR+JH4hPyE/I0AjQCqBKoEzwjPCNkM2QzAEMAQehR6FN8X3xfyGvIavB28HSAgICA0IjQiESQRJJ8lnyXyJvImISghKAspCym4KbgpNio2KlwqXCouKi4qvCm8Keko6SjCJ8InZSZlJsYkxiT9Iv0iKiEqIUcfRx9jHWMdkxuTG8sZyxkOGA4YaxZrFtUU1RRPE08T5RHlEYwQjBBID0gPHg4eDgUNBQ38C/wLBgsGCxgKGAosCSwJPwg/CEgHSAdFBkUGNwU3BSIEIgQKAwoD9gH2AesA6wDn/+f/6f7p/vH98f30/PT86Pvo+876zvqX+Zf5PPg8+Mj2yPY39Tf1jvOO8+bx5vFH8Efwt+637lXtVe0j7CPsHuse61zqXOrX6dfpgumC6Wvpa+mM6Yzp2enZ6V/qX+oe6x7rDOwM7DbtNu2d7p3uOPA48AvyC/IS9BL0QfZB9pH4kfj8+vz6ev16/f////+OAo4CJAUkBbIHsgc8CjwKwgzCDC0PLQ9+EX4RtBO0E7AVsBVvF28X8BjwGBcaFxrkGuQaYxtjG4MbgxtMG0wb1BrUGg4aDhoGGQYZ0BfQF2MWYxbHFMcUDBMMEy0RLREsDywPFQ0VDeYK5gqjCKMIVQZVBgcEBwS9Ab0Bgv+C/2X9Zf1q+2r7lfmV+fP38/eE9oT2RPVE9UH0QfR283bz3PLc8n3yffJR8lHyTfJN8nnyefLM8szyPvM+89Lz0vOC9IL0QfVB9Q/2D/bk9uT2t/e394j4iPhV+VX5GfoZ+tf61/qP+4/7Pfw9/N/83/xy/XL98P3w/VT+VP6X/pf+sv6y/qP+o/5m/mb+//3//Xn9ef3f/N/8PPw8/Kb7pvsn+yf7yPrI+pX6lfqN+o36qfqp+uP64/ot+y37eft5+777vvv4+/j7JPwk/Er8Svx0/HT8q/yr/Pn8+fxn/Wf99/33/af+p/51/3X/VgBWAD0BPQEdAh0C7wLvAqQDpAM8BDwEtgS2BBAFEAVOBU4FcwVzBX0FfQVsBWwFQQVBBfYE9gSKBIoE/gP+A1UDVQORApECuwG7AdoA2gD2//b/FP8U/z3+Pf5v/W/9qPyo/Oz77Ps1+zX7fPp8+sr5yvkb+Rv5cPhw+NT31PdI90j3zvbO9nP2c/Y39jf2G/Yb9ib2JvZU9lT2nvae9gP3A/eA94D3CfgJ+KL4ovhF+UX57Pns+ZT6lPo5+zn70vvS+178XvzZ/Nn8Pv0+/Y79jv3G/cb95/3n/fX99f30/fT96P3o/dj92P3G/cb9tP20/aP9o/2T/ZP9gP2A/Wv9a/1Q/VD9Lf0t/QT9BP3V/NX8ofyh/HD8cPxG/Eb8Jvwm/Bb8Fvwb/Bv8NPw0/GT8ZPyq/Kr8BP0E/XL9cv3y/fL9gP6A/hn/Gf+5/7n/WwBbAPsA+wCXAZcBLAIsArUCtQIxAzEDoAOgA/sD+wNDBEMEeQR5BJsEmwSrBKsEqwSrBJcElwRzBHMEQARABP0D/QOtA60DUgNSA+4C7gJ+An4CBgIGAoQBhAH4APgAZABkAMr/yv8s/yz/jP6M/vH98f1e/V791/zX/GT8ZPwG/Ab8vfu9+477jvt1+3X7b/tv+3v7e/uU+5T7tPu0+9z73PsF/AX8LPws/FL8Uvx1/HX8lfyV/Lb8tvzZ/Nn8AP0A/S79Lv1j/WP9nf2d/dr92v0Z/hn+VP5U/oz+jP68/rz+5f7l/gX/Bf8d/x3/Lv8u/z7/Pv9Q/1D/af9p/4z/jP+7/7v/9//3/z8APwCRAJEA7gDuAFQBVAHCAcIBOwI7AroCugJCA0ID0gPSA2QEZAT4BPgEjgWOBRsGGwahBqEGHwcfB5AHkAf0B/QHUghSCKMIowjnCOcIHwkfCUAJQAk8CTwJCgkKCZMIkwjBB8EHhQaFBtME0wSiAqIC9//3/+787vyZ+Zn5FvYW9pnymfI57znvC+wL7DjpOOm75rvmhuSG5Kfip+L94P3gZt9m3/Hd8d2H3IfcF9sX283Zzdmt2K3Yvte+1zjXONcr1yvXlteW157YnthA2kDaY9xj3ArfCt8j4iPijeWN5T/pP+ky7TLtVPFU8Zr1mvUH+gf6kP6Q/h0DHQOuB64HNQw1DI8QjxC4FLgUqxirGEEcQRyCH4IfeyJ7IgwlDCVKJ0onUClQKf8q/ypqLGospy2nLY0ujS4iLyIvdC90L1gvWC/TLtMu+y37LbUstSwRKxErMSkxKQ0nDSe7JLskYSJhIvcf9x+RHZEdRxtHGw4ZDhnvFu8W9xT3FBsTGxNeEV4Rxg/GD0gOSA7gDOAMkAuQC0sKSwoLCQsJzwfPB5AGkAZLBUsFAgQCBLgCuAJwAXABMAAwAAP/A//q/er95/zn/P/7//so+yj7VvpW+ob5hvmn+Kf4qPeo9472jvZP9U/15vPm82jyaPLY8NjwP+8/77ztvO1X7FfsFOsU6xDqEOpL6UvpvOi86HjoeOh26Hboqeip6CDpIOnU6dTpveq96uTr5OtJ7Unt4O7g7q3wrfCt8q3y0vTS9Bn3Gfd++X75+Pv4+3z+fP4LAQsBoQOhAzAGMAbACMAIVAtUC9kN2Q1TEFMQwRLBEgIVAhUUFxQX8RjxGHgaeBqjG6Mbehx6HOEc4RziHOIcjxyPHN8b3xviGuIashmyGUsYSxi7FrsWFBUUFVITUhN2EXYRiw+LD4sNiw10C3QLTQlNCRgHGAfYBNgElQKVAlwAXAAw/jD+GfwZ/CX6JfpZ+Fn4tfa19kr1SvUX9Bf0GPMY81jyWPLU8dTxgvGC8WzxbPGJ8Ynxy/HL8TbyNvK98r3yUPNQ8+/z7/OQ9JD0KvUq9cD1wPVO9k720fbR9lD3UPfM98z3Q/hD+Lr4uvgw+TD5nfmd+QL6AvpX+lf6kvqS+rf6t/rB+sH6sfqx+pP6k/pt+m36SfpJ+jP6M/o1+jX6UvpS+pD6kPrt+u36YPtg++b75vtz/HP8//z//Ib9hv0I/gj+h/6H/gf/B/+S/5L/LQAtANcA1wCWAZYBZQJlAj0DPQMWBBYE6QTpBKUFpQVCBkIGuga6BgUHBQclByUHIwcjBwAHAAfEBsQGeAZ4Bh0GHQazBbMFPQU9BbcEtwQeBB4EbwNvA6kCqQLIAcgBzwDPAMT/xP+s/qz+jf2N/XT8dPxi+2L7Wvpa+mf5Z/mK+Ir4w/fD9xv3G/eR9pH2IPYg9s/1z/Wd9Z31g/WD9Yz1jPW09bT1+PX49Vr2WvbX9tf2ZPdk9wP4A/it+K34W/lb+Q36Dfq/+r/6bPts+xb8Fvy7/Lv8Wf1Z/e/97/15/nn+9P70/l7/Xv+2/7b//P/8/y8ALwBPAE8AXQBdAFoAWgBGAEYAIgAiAPH/8f+z/7P/a/9r/xr/Gv/D/sP+af5p/hH+Ef69/b39c/1z/Tf9N/0P/Q/9/Pz8/AL9Av0g/SD9Uv1S/Zf9l/3o/ej9Qv5C/qD+oP4C/wL/Z/9n/83/zf84ADgAqQCpAB8BHwGbAZsBHgIeAqACoAIgAyADmgOaAwUEBQReBF4EoQShBMgEyATRBNEEvQS9BIwEjARABEAE3wPfA2oDagPkAuQCUAJQAq8BrwECAQIBTABMAJH/kf/R/tH+Df4N/kr9Sv2I/Ij8x/vH+w/7D/tf+l/6ufm5+ST5JPmg+KD4K/gr+M/3z/eK94r3WfdZ90L3QvdE90T3WPdY94L3gve+9773BPgE+Ff4V/ix+LH4DvkO+XH5cfnZ+dn5RfpF+rn6ufo2+zb7ufu5+0T8RPzW/Nb8bf1t/Qf+B/6j/qP+Pf89/9P/0/9kAGQA7wDvAHQBdAHzAfMBbwJvAuYC5gJcA1wD0wPTA0kESQTBBMEEPQU9BbcFtwUyBjIGsQaxBioHKgehB6EHFggWCIEIgQjgCOAINQk1CXYJdgmiCaIJwAnACcoJygnFCcUJuwm7CacJpwmMCYwJZwlnCSsJKwnGCMYIJwgnCDsHOwflBeUFFwQXBM8BzwEC/wL/vfu9+yb4JvhU9FT0bfBt8LHssew56TnpFuYW5nHjceM94T3hXN9c39jd2N2F3IXcOds52wHaAdrI2MjYgNeA11rWWtZo1WjVtdS11IHUgdTg1ODU0dXR1XHXcde42bjZgtyC3MjfyN9w43DjVedV52vra+ut763vDvQO9Ij4iPgk/ST93gHeAZ4GngZjC2MLIRAhEKkUqRT1GPUY/hz+HJcglyDMI8wjrSatJh8pHyk9Kz0rKS0pLcYuxi4pMCkwaDFoMVMyUzLsMuwyPjM+MxgzGDN/Mn8yijGKMR4wHjBSLlIuTixOLAoqCiqhJ6EnOCU4JccixyJdIF0gDx4PHs4bzhugGaAZkheSF5YVlhWtE60T3xHfESAQIBBpDmkOvAy8DAsLCwtQCVAJiQeJB7QFtAXQA9AD5QHlAQAAAAAp/in+bfxt/Nr62vpy+XL5L/gv+BP3E/cL9gv2AvUC9fXz9fPQ8tDyhvGG8SXwJfCu7q7uJe0l7a7rrutS6lLqFekV6RboFuhS51LnwObA5nLmcuZe5l7md+Z35szmzOZa51rnGuga6B/pH+ls6mzq/Ov869Xt1e3z7/PvRPJE8rz0vPRO90735vnm+XX8dfz4/vj+bAFsAcsDywMlBiUGgwiDCN4K3gpBDUENrg+uDwgSCBJJFEkUahZqFkQYRBjOGc4ZBhsGG88bzxssHCwcLhwuHMsbyxsUGxQbJhomGv0Y/RiqF6oXQRZBFrsUuxQdEx0TchFyEbIPsg/gDeANAwwDDBoKGgonCCcIMQYxBj8EPwRPAk8CaABoAJH+kf7J/Mn8E/sT+3v5e/n+9/73n/af9mz1bPVm9Gb0jPOM8+vy6/J+8n7yOvI68ibyJvI28jbyWfJZ8pPyk/LX8tfyGfMZ817zXvOi86Lz4PPg8yL0IvRo9Gj0rfSt9Pv0+/RK9Ur1lPWU9d313fUf9h/2VfZV9ob2hva09rT23/bf9hb3Fvdg92D3wffB90T4RPjt+O34tvm2+Zv6m/qS+5L7jfyN/ID9gP1j/mP+M/8z/+//7/+jAKMAWwFbAR8CHwL9Av0C+gP6Aw4FDgU2BjYGaQdpB5EIkQihCaEJjwqPCkMLQwu5C7kL8wvzC+8L7wu4C7gLYAtgC+oK6gpgCmAKywnLCSYJJgluCG4IpwenB8oGygbWBdYFzATMBK0DrQN7AnsCOAE4Ae3/7f+c/pz+R/1H/fj7+Pus+qz6Zvlm+TH4MfgR9xH3CfYJ9ir1KvV29Hb06/Pr85Pzk/No82jzY/Nj84jziPPQ89DzMPQw9Kr0qvQ39Tf1zvXO9XT2dPYm9yb34vfi96r4qvh7+Xv5TPpM+hn7Gfvb+9v7i/yL/Cb9Jv2s/az9Hf4d/n3+ff7R/tH+HP8c/2D/YP+f/5//1//X/wQABAAiACIAKQApABYAFgDl/+X/lv+W/y//L/+3/rf+Of45/sP9w/1d/V39EP0Q/eL84vzT/NP84Pzg/AP9A/03/Tf9c/1z/bL9sv30/fT9N/43/n7+fv7O/s7+J/8n/4r/iv/5//n/cgByAPIA8gB2AXYB/QH9AX8CfwL7AvsCbwNvA9YD1gMxBDEEfgR+BLgEuATcBNwE6AToBNgE2ASrBKsEYQRhBP0D/QN+A34D7gLuAlACUAKqAaoBAwEDAV4AXgC7/7v/GP8Y/3X+df7N/c39Hf0d/Wv8a/y1+7X7/Pr8+kj6SPqa+Zr59fj1+GP4Y/jl9+X3evd69yj3KPfs9uz2vva+9qP2o/aV9pX2ivaK9or2ivaR9pH2nfad9rj2uPbh9uH2FvcW91/3X/e597n3Ivgi+Jz4nPgn+Sf5wPnA+Wj6aPoc+xz72fvZ+538nfxp/Wn9Pf49/hf/F//7//v/6gDqAN4B3gHaAtoC3APcA90E3QTeBd4F3gbeBtIH0ge8CLwInAmcCWYKZgobCxsLvQu9C0IMQgytDK0MBg0GDUUNRQ1vDW8Niw2LDY8Njw17DXsNUw1TDQkNCQ2WDJYM9Qv1CxILEgvZCdkJPQg9CDQGNAasA6wDpQClADL9Mv1X+Vf5LPUs9efw5/Cm7KbsiOiI6M7kzuSF4YXhrt6u3mTcZNyM2oza/dj92L7Xvteo1qjWlNWU1Z7UntS507nT39Lf0k3STdIY0hjSTtJO0ijTKNOz1LPU4Nbg1rjZuNko3SjdAuEC4TXlNeWp6anpP+4/7uby5vKg96D3Y/xj/CEBIQHlBeUFqQqpClAPUA/YE9gTOBg4GEIcQhzzH/MfUCNQIzEmMSamKKYoyyrLKogsiCz5LfktQC9ALz4wPjACMQIxnjGeMeMx4zHTMdMxezF7MbQwtDCHL4cvEi4SLkUsRSw6KjoqHCgcKOcl5yW1I7UjpyGnIbEfsR/cHdwdNxw3HKsaqxo1GTUZ2hfaF4QWhBYsFSwV1xPXE3MScxL6EPoQcA9wD8oNyg0GDAYMLAosCj8IPwhEBkQGSQRJBFgCWAJ3AHcAqv6q/vj8+PxV+1X7t/m3+Rv4G/hu9m72n/Sf9Lfyt/Kt8K3wgu6C7lfsV+w16jXqI+gj6EzmTOa25LbkXuNe42PiY+K+4b7hYeFh4V7hXuGr4avhOOI44hXjFeNC5ELksuWy5XXndeeL6Yvp5uvm64Pug+5b8VvxWPRY9Gv3a/eL+ov6q/2r/b0AvQDEA8QDwwbDBqwJrAmLDIsMZQ9lDyESIRLBFMEURBdEF4IZghlzG3MbFB0UHT8ePx7xHvEeNx83H/4e/h5WHlYeWR1ZHQgcCBx0GnQauxi7GNgW2BbWFNYUwhLCEpgQmBBYDlgOCQwJDK4JrglIB0gH4gTiBIcChwI9AD0AC/4L/v77/vsX+hf6V/hX+Mj2yPZm9Wb1LPQs9CPzI/NF8kXyifGJ8fzw/PCb8JvwYvBi8FvwW/CC8ILwy/DL8DvxO/HG8cbxXvJe8gfzB/O387fzZvRm9BX1FfXA9cD1XfZd9u/27/Zx93H32/fb9zH4Mfhw+HD4k/iT+KT4pPiq+Kr4rPis+Ln4ufjf+N/4J/kn+Zr5mvk9+j36CvsK+/j7+Pv5/Pn8+f35/eb+5v6z/7P/WwBbAN8A3wBOAU4BugG6ATICMgLMAswCkwOTA4QEhASeBZ4F1wbXBhYIFghMCUwJagpqClYLVgsJDAkMhQyFDMMMwwzQDNAMvQy9DIsMiwxDDEMM6wvrC3oLegvrCusKPwo/Cm4Jbgl3CHcIYQdhBy0GLQbgBOAEgwODAx0CHQKxALEAQ/9D/9r92v1w/HD8BPsE+535nfk8+Dz44/bj9qT1pPWC9IL0f/N/86ryqvIB8gHyffF98SvxK/EF8QXxAfEB8SXxJfFp8WnxxfHF8T7yPvLR8tHyePN48zf0N/QI9Qj13/Xf9bz2vPaX95f3aPho+DH5Mfny+fL5qvqq+mH7Yfsc/Bz83vze/Kn9qf16/nr+TP9M/xYAFgDOAM4AbAFsAekB6QFAAkACcwJzAoMCgwJ5AnkCYQJhAkUCRQIxAjECLAIsAjQCNAJJAkkCYwJjAnkCeQKFAoUCggKCAmwCbAJFAkUCEQIRAtMB0wGTAZMBVwFXASEBIQHzAPMAzADMAKkAqQCJAIkAaABoAEUARQAfAB8A+P/4/9L/0v+x/7H/lv+W/4X/hf97/3v/eP94/3f/d/90/3T/aP9o/1L/Uv8w/zD/BP8E/9X+1f6p/qn+hP6E/mz+bP5i/mL+Zv5m/nT+dP6I/oj+nf6d/q7+rv61/rX+rv6u/pf+l/5w/nD+Of45/vn9+f2z/bP9aP1o/R39Hf3Q/ND8e/x7/CD8IPy4+7j7PPs8+7L6svoY+hj6bvlu+cL4wvga+Br4ePd49+z27PZ39nf2GPYY9tf11/W09bT1p/Wn9bn1ufXn9ef1LfYt9pX2lfYj9yP32ffZ97z4vPjR+dH5E/sT+3/8f/wP/g/+uv+6/3ABcAErAysD4wTjBIsGiwYkCCQIsgmyCSkLKQuMDIwM4A3gDRMPEw8mECYQHBEcEeMR4xF6EnoS5RLlEg8TDxP5EvkSqxKrEhcSFxI3ETcRCBAIEHIOcg5XDFcMqAmoCV8GXwZsAmwC3/3f/eL44viO847zD+4P7rnouei147XjKN8o31PbU9ss2CzYktWS1YzTjNPk0eTRYdBh0BLPEs/bzdvNpMykzKfLp8vzyvPKlsqWytzK3Mriy+LLps2mzUnQSdC9073Tz9fP12rcatxs4WzhoOag5u/r7+tO8U7xrvau9gT8BPxeAV4BvAa8BgQMBAw2ETYRRxZHFgEbARtfH18fWiNaI8QmxCamKaYpFywXLPkt+S1rL2svlTCVMF4xXjHhMeExOTI5Mj0yPTL0MfQxbjFuMYEwgTA1LzUvoy2jLbcrtyuIKYgpPyc/J9kk2SRzInMiLiAuIAMeAx4BHAEcPxo/Gq4YrhhWF1YXQhZCFlwVXBWdFJ0UBBQEFHYTdhPoEugSVRJVEqURpRHNEM0Qzg/OD54Ong5EDUQN0gvSC04KTgrDCMMIQAdAB8YFxgVTBFME4QLhAmkBaQHX/9f/Gf4Z/iz8LPwC+gL6mfeZ9w31DfVk8mTyru+u7xftF+2n6qfqaOho6HzmfObg5ODkieOJ447ijuLk4eThfeF94W/hb+G74bvhWeJZ4l/jX+PR5NHko+aj5tvo2+hy63LrTu5O7lzxXPGN9I30xvfG9/f69/oi/iL+RwFHAV8EXwR5B3kHmwqbCrQNtA3JEMkQ1RPVE68WrxZNGU0ZoBugG3sdex3XHtcevB+8HxEgESDoH+gfWx9bH2UeZR4cHRwdmhuaG9kZ2RnjF+MXxRXFFXcTdxP5EPkQWQ5ZDpoLmgvHCMcI8gXyBSsDKwN7AHsA6/3r/Yj7iPtN+U35Nfc190n1SfWA84Dz1fHV8VvwW/AT7xPv/e397TLtMu2w7LDscuxy7ITshOzb7NvsZO1k7R3uHe7z7vPu0e/R77jwuPCh8aHxhfKF8m/zb/Nf9F/0T/VP9UL2QvYv9y/3BvgG+MT4xPhe+V75y/nL+RP6E/o9+j36VvpW+nb6dvqw+rD6E/sT+6v7q/t4/Hj8cP1w/YH+gf6V/5X/lACUAGsBawESAhICiwKLAuEC4QIqAyoDgAOAA/AD8AOLBIsEVgVWBUQGRAZMB0wHXgheCF8JXwlDCkMKAQsBC44LjgvxC/ELOww7DHAMcAyeDJ4MzwzPDPUM9QwKDQoNBA0EDc4MzgxeDF4MswuzC8sKywqqCaoJYwhjCAEHAQeUBZQFLAQsBNEC0QKFAYUBQgBCAAb/Bv/F/cX9dvx2/B77Hvu7+bv5UfhR+PH28fag9aD1ZPRk9FDzUPNn8mfyp/Gn8RzxHPHC8MLwj/CP8IjwiPCl8KXw2vDa8C3xLfGW8ZbxCvIK8o3yjfIa8xrzpfOl8zX0NfTG9Mb0VfVV9en16fWG9ob2K/cr9+D34Peo+Kj4gfmB+Wv6a/pj+2P7Yfxh/Fv9W/1L/kv+KP8o/+//7/+fAJ8APQE9Ac0BzQFZAlkC6QLpAn4DfgMdBB0ExQTFBGwFbAUOBg4GqAaoBi0HLQeZB5kH7wfvBygIKAhICEgIVghWCE4ITgg0CDQICggKCMoHygdvB28H+Ab4Bl4GXgadBZ0FugS6BLkDuQOfAp8CdwF3AUsASwAj/yP/BP4E/vf89/z8+/z7EPsQ+zf6N/ps+Wz5qPio+PP38/dI90j3qfap9iL2Iva39bf1a/Vr9Uj1SPVS9VL1g/WD9eH14fVo9mj2DvcO99H30fep+Kn4i/mL+XT6dPpe+177Q/xD/CL9Iv34/fj9wv7C/nz/fP8iACIArQCtABgBGAFdAV0BewF7AW8BbwE7ATsB4wDjAG4AbgDh/+H/Rf9F/6L+ov78/fz9WP1Y/bz8vPwp/Cn8n/uf+yT7JPu6+rr6Yvpi+ij6KPoS+hL6JPok+mT6ZPrW+tb6dvt2+0P8Q/w7/Tv9V/5X/pH/kf/nAOcAUwJTAskDyQNIBUgFzAbMBkUIRQiyCbIJDAsMCz8MPwxJDUkNLg4uDucO5w6BD4EPCRAJEHAQcBCcEJwQaxBrEKkPqQ8rDisO4gviC9QI1AgGBQYFjQCNAJL7kvsm9ib2c/Bz8NLq0up35XflkeCR4GHcYdzY2NjYzNXM1UXTRdMP0Q/R/M78ziHNIc1ay1rLhsmGydzH3MdyxnLGacVpxSrFKsXexd7FgseCxy7KLsrFzcXNDdIN0vfW99Zm3GbcJuIm4hnoGegq7iruP/Q/9E/6T/pyAHIArgauBukM6QwfEx8TOxk7Gfoe+h5RJFEkOik6KXkteS0TMRMxGDQYNGE2YTYUOBQ4ZDlkOT46Pjq/Or86CDsIO+Q65DpXOlc6czlzOQo4CjgiNiI21TPVMwYxBjHMLcwtVCpUKqcmpybrIusiTB9MH9Eb0RuNGI0YlhWWFekS6RKJEIkQfA58DrMMswwjCyMLygnKCZsImwiNB40HmgaaBrIFsgXHBMcE0gPSA9IC0gLNAc0BzQDNAN7/3v8C/wL/P/4//pj9mP0K/Qr9j/yP/CD8IPyp+6n7EvsS+1D6UPpW+Vb5IPgg+MX2xfZN9U31xvPG81DyUPL28Pbwv++/78Xuxe4H7gfufO187S3tLe0T7RPtIu0i7W3tbe337fftw+7D7t/v3+9T8VPxGPMY8yv1K/WC94L3BvoG+pz8nPwx/zH/tQG1ARcEFwReBl4GlAiUCLYKtgrXDNcMAw8DDywRLBFYE1gThBWEFYcXhxdRGVEZ1BrUGuQb5Bt6HHocoRyhHEocShyHG4cbdBp0GhQZFBl6F3oXvhW+FdwT3BPXEdcRtQ+1D24Nbg0BCwELdQh1CNQF1AUlAyUDeAB4AOD94P1l+2X7D/kP+e327fb79Pv0MfMx85nxmfEm8Cbwzu7O7qPto+2g7KDswuvC6yDrIOu56rnqiuqK6qHqoer56vnqf+t/6zXsNewI7Qjt4O3g7cDuwO6d753vbPBs8DfxN/H88fzxtvK28m3zbfMh9CH0zPTM9Hb1dvUg9iD2yPbI9nf3d/c3+Df4DvkO+Qb6Bvoo+yj7cPxw/NX91f1N/03/xADEACYCJgJkA2QDeQR5BF0FXQUfBh8G1AbUBokHiQdYCFgIVAlUCXgKeArFC8ULMg0yDZoOmg7qD+oPEBEQEecR5xFqEmoSnhKeEn8SfxIkEiQSqBGoERERERFvEG8Qzg/ODx8PHw9dDl0OhA2EDYYMhgxbC1sLBgoGCoYIhgjdBt0GFgUWBT4DPgNfAV8Bg/+D/7j9uP39+/37T/pP+rj4uPgy9zL3u/W79WT0ZPQs8yzzFfIV8i7xLvF58Hnw8u/y76bvpu+R75Hvqu+q7/jv+O9x8HHwBfEF8bbxtvF48njyP/M/8wv0C/TX9Nf0mfWZ9Vb2VvYM9wz3tve291r4Wvj5+Pn4kvmS+Sv6K/rH+sf6Zftl+wf8B/yr/Kv8TP1M/ef95/13/nf++P74/mX/Zf++/77/BAAEADkAOQBoAGgAmQCZANQA1AAiASIBhwGHAQECAQKPAo8CLgMuA9ID0gN2BHYEFQUVBaQFpAUgBiAGjQaNBugG6AY2BzYHfwd/B74Hvgf0B/QHIAggCDYINggtCC0IBAgECK4HrgcrBysHfgZ+BqkFqQWyBLIEpgOmA48CjwJyAXIBVwBXAEP/Q/8z/jP+If0h/RD8EPz4+vj61vnW+bP4s/iP94/3bvZu9mH1YfVr9Gv0kPOQ89/y3/JY8ljy9fH18cDxwPGx8bHxu/G78eTx5PEl8iXydPJ08tny2fJR81Hz1PPU82j0aPQK9Qr1sfWx9WX2ZfYj9yP35/fn97f4t/iS+ZL5dPp0+l77XvtL/Ev8N/03/Rz+HP74/vj+yv/K/5AAkABNAU0BBwIHAr0CvQJ2A3YDOQQ5BAEFAQXUBdQFsgayBo8HjwdqCGoIPgk+Cf0J/QmlCqUKOws7C7QLtAsWDBYMaAxoDKAMoAzDDMMM1AzUDMkMyQynDKcMcwxzDCYMJgzAC8ALQgtCC6AKoArLCcsJtgi2CE8HTwd4BXgFHQMdAzgAOACz/LP8kviS+P3z/fMG7wbv2OnY6cnkyeQC4ALgptum2wDYANgN1Q3Vt9K30g7RDtHdz93P4s7iziDOIM5ozWjNlMyUzNTL1MswyzDLtsq2yrjKuMpay1rLqcypzNbO1s7h0eHRo9Wj1QzaDNr33vfeKuQq5IjpiOn/7v/uevR69Oz57Plf/1//1ATUBDYKNgqLD4sPzBTMFMcZxxlzHnMexiLGIogmiCa/Kb8pfix+LKkuqS5lMGUw3zHfMQAzADPqM+ozujS6NEY1RjWTNZM1rTWtNWE1YTWtNK00pTOlMycyJzJEMEQwJC4kLrsruysmKSYpjyaPJu4j7iNZIVkh7B7sHpocmhxuGm4adBh0GJ4WnhbqFOoUWhNaE9oR2hFaEFoQ0Q7RDikNKQ1QC1ALQwlDCf8G/waIBIgE7QHtAUf/R/+l/KX8GfoZ+r33vfeW9Zb1oPOg8+Tx5PFP8E/wye7J7lftV+3k6+TrZOpk6u/o7+iJ54nnOeY55iblJuVe5F7k4+Pj49Pj0+Mr5Cvk3OTc5O3l7eVT51Pn/Oj86O7q7uor7Svtr++v74DygPKl9aX1GfkZ+cr8yvyxALEAtQS1BK0IrQiFDIUMKRApEHATcBNbFlsW8xjzGCUbJRsLHQsdvx6/HjMgMyB5IXkhoiKiIokjiSMqJCokhiSGJHEkcSTmI+Yj7yLvIngheCGMH4wfSh1KHbMasxraF9oX2xTbFLsRuxGEDoQORQtFCwkICQjTBNMErwGvAa7+rv7V+9X7L/kv+c32zfaw9LD00fLR8jrxOvHd793vp+6n7p7tnu217LXs3uve6yzrLOud6p3qLuou6vbp9un46fjpK+or6pzqnOo/6z/r/ev969bs1uy37bftje6N7l/vX+8n8Cfw3vDe8JPxk/FA8kDy3fLd8m3zbfPj8+PzM/Qz9GL0YvRw9HD0X/Rf9En0SfRD9EP0YvRi9MP0w/R89Xz1k/aT9gn4CfjR+dH5zfvN+9n92f3T/9P/oAGgASoDKgN1BHUEjgWOBYYGhgZ6B3oHhwiHCLEJsQkECwQLgQyBDA0ODQ6bD5sPHREdEW4SbhKGE4YTZBRkFPcU9xRJFUkVbBVsFVkVWRUWFRYVrRStFAsUCxQsEywTFBIUErUQtRAWDxYPRA1EDUkLSQs0CTQJGwcbBw0FDQUSAxIDMwEzAXT/dP/M/cz9M/wz/Kn6qfok+ST5nvee9yT2JPa29Lb0VPNU8xPyE/L18PXw+u/67zfvN++v7q/uXO5c7k7uTu597n3u2+7b7m3vbe8n8Cfw+fD58OPx4/Hb8tvy0vPS88j0yPSz9bP1ivaK9lL3UvcL+Av4tvi2+F35XfkH+gf6ufq5+nj7ePtK/Er8LP0s/Rv+G/4X/xf/FwAXABQBFAEJAgkC8gLyAskDyQORBJEETQVNBfkF+QWbBpsGNwc3B8UHxQdICEgIvwi/CCAJIAlpCWkJmwmbCa4JrgmjCaMJggmCCUkJSQn/CP8IqwirCEkISQjWB9YHTwdPB6kGqQbdBd0F6AToBMoDygOEAoQCHwEfAar/qv8u/i7+ufy5/F77Xvsp+in6Hvke+Uv4S/ip96n3L/cv99n22faa9pr2ZfZl9jv2O/YX9hf29fX19d713vXT9dP10fXR9eT15PUN9g32RvZG9pj2mPb79vv2afdp99733vdU+FT4wPjA+CH5Iflw+XD5p/mn+cj5yPnR+dH5v/m/+Zv5m/ll+WX5Hvke+dD40Ph/+H/4Lvgu+Ov36/e797v3ofeh96f3p/fP98/3GfgZ+In4ifgk+ST56vnq+dz63PoA/AD8Uf1R/cv+y/5uAG4AMQIxAgYEBgTqBeoF1AfUB7EJsQl/C38LPA08DdMO0w5EEEQQkxGTEawSrBKXE5cTWRRZFOMU4xQ1FTUVVBVUFS8VLxW/FL8UAxQDFOAS4BI3ETcR8g7yDvcL9wsmCCYIgAOAAyX+Jf4k+CT4pvGm8fzq/OpM5EzkyN3I3dPX09eO0o7SB84HznbKdsq3x7fHjcWNxQPEA8Tewt7C5MHkwTfBN8HDwMPAdcB1wJPAk8A3wTfBbsJuwoXEhcSSx5LHf8t/y1PQU9Du1e7VEtwS3JnimeJf6V/pNfA18Pf29/af/Z/9JQQlBHIKcgqXEJcQlhaWFkEcQRyVIZUhhSaFJs8qzypwLnAucTFxMaUzpTMlNSU1HjYeNoA2gDZ5Nnk2PzY/NsA1wDUUNRQ1WjRaNGUzZTM3Mjcy2zDbMCcvJy8cLRwt0yrTKjwoPChyJXIlnyKfIsgfyB8HHQcdexp7GiAYIBgBFgEWMRQxFKISohJaEVoRWxBbEI4Pjg/oDugOXQ5dDs8Nzw0qDSoNXgxeDFULVQsACgAKYAhgCHgGeAZPBE8E+QH5AY//j/8h/SH9wfrB+oX4hfhr9mv2avRq9IjyiPKu8K7wyO7I7uXs5ez96v3qE+kT6U3nTee65brlYuRi5G7jbuPi4uLit+K34gDjAOOx47Hjt+S35BfmF+bP58/n0+nT6S/sL+zs7uzuCPII8oH1gfVa+Vr5gf2B/dMB0wE+Bj4GoQqhCskOyQ6kEqQSJxYnFjAZMBnMG8wbEh4SHvEf8R+GIYYh7CLsIg0kDSTzJPMkqyWrJQ4mDiYaJhom1SXVJSAlICX9I/0jfiJ+IpUglSBPHk8exRvFG/cY9xj0FfQV1BLUEqAPoA9fDF8MJAkkCfkF+QXjAuMC8f/x/zT9NP2y+rL6cPhw+Hz2fPbO9M70WfNZ8yDyIPIQ8RDxFPAU8DTvNO9j7mPulO2U7dzs3Ow57DnsqOuo60LrQusE6wTr6Oro6vvq++ox6zHreet569nr2etD7EPspeyl7ArtCu1r7Wvtvu2+7RbuFu5v7m/uxO7E7iPvI++J74nv8O/w72bwZvD08PTwofGh8YPyg/Ks86zzH/Uf9eP24/b0+PT4P/s/+6r9qv0hACEAjAKMAs8EzwTlBuUGywjLCHoKegr/C/8LaA1oDa8Orw7jD+MPDhEOER0SHRIRExET6RPpE4cUhxTmFOYUCRUJFd8U3xRyFHIU0hPSE/wS/BL5EfkR1hDWEIoPig8VDhUOgAyADMgKyArxCPEIBAcEBwsFCwULAwsDEAEQASj/KP9Z/Vn9rfut+zT6NPrt+O342vfa9wD3APdX9lf21PXU9Xb1dvUv9S/18vTy9MD0wPSW9Jb0bvRu9Fn0WfRa9Fr0c/Rz9K70rvQI9Qj1dfV19ff19/WB9oH2B/cH94j3iPf79/v3V/hX+KH4ofjW+Nb4+fj5+BP5E/kq+Sr5QvlC+WP5Y/mQ+ZD5xvnG+Qv6C/pe+l76wfrB+jj7OPvG+8b7avxq/CX9Jf34/fj94P7g/tj/2P/jAOMA/wH/ASYDJgNaBFoEmAWYBdIG0gYDCAMIIwkjCSAKIArzCvMKnQudCxIMEgxXDFcMdQx1DGoMagw6DDoM8QvxC4kLiQsBCwELXgpeCpYJlgmmCKYIjwePB08GTwblBOUEVQNVA6kBqQHk/+T/Ev4S/kf8R/yM+oz67Pjs+Hn3efc19jX2IPUg9Ub0RvSh86HzKvMq8+jy6PLQ8tDy1PLU8vPy8/Ii8yLzWPNY85zznPPt8+3zSPRI9Lb0tvQ19TX1vPW89U72Tvbh9uH2aPdo9+X35fdO+E74oPig+OD44PgR+RH5M/kz+VH5Ufls+Wz5hPmE+Z/5n/m++b753Pnc+QH6Afor+iv6XPpc+pn6mfrs+uz6WftZ++v76/uq/Kr8mP2Y/bX+tf4AAAAAcQFxAfgC+AKRBJEEMgYyBswHzAdfCV8J7QrtCmkMaQzVDdUNMg8yD2sQaxB8EXwRZRJlEhATEBN8E3wTrBOsE5ATkBMlEyUTcBJwEmMRYxHzD/MPHg4eDtYL1gsCCQIJkwWTBYoBigHQ/ND8bPds947xjvFK60rrzuTO5H7eft6I2IjYFNMU03rOes6/yr/KzcfNx8bFxsV4xHjEo8Ojw1HDUcNVw1XDf8N/w/rD+sPGxMbE4cXhxY/Hj8fnyefJ58znzLDQsNA81TzVX9pf2gHgAeD95f3lHewd7DzyPPJK+Er4Nv42/uoD6gNyCXIJ0A7QDuQT5BOzGLMYNh02HTshOyG8JLwkuCe4JwEqASqhK6Ertiy2LC4tLi0xLTEt8izyLGwsbCzEK8QrIisiK3AqcCq6KbopECkQKUwoTChrJ2sneyZ7Jl4lXiUiJCIk4SLhIo8hjyE+ID4gBh8GH9Qd1B2wHLAcqBuoG6capxq0GbQZ3RjdGA4YDhhLF0sXmhaaFuIV4hUZFRkVNxQ3FB0THRO2EbYR+A/4D9MN0w09Cz0LPwg/CO4E7gRZAVkBoP2g/fD58Plc9lz29/L38t7v3u8G7QbtX+pf6vjn+Oe85bzln+Of47/hv+Eg4CDgx97H3uHd4d183Xzdld2V3UneSd6R35HfU+FT4Y3jjeMt5i3mFukW6UHsQeyr76vvSfNJ8xf3F/cg+yD7Yf9h/8UDxQNJCEkI1wzXDDwRPBFiFWIVNRk1GX4cfhw6HzofcCFwIQYjBiMTJBMktiS2JOIk4iSwJLAkOiQ6JG8jbyNZIlkiDCEMIXUfdR+eHZ4dmRuZG14ZXhn3FvcWeBR4FOQR5BFGD0YPtAy0DDIKMgrHB8cHewV7BVQDVANRAVEBev96/9b91v1p/Gn8OPs4+0r6Svqc+Zz5Jvkm+eX45fjJ+Mn4wPjA+MD4wPi7+Lv4pPik+H74fvhE+ET48/fz95T3lPck9yT3ovai9hj2GPaC9YL12vTa9Cz0LPRv82/znvKe8sbxxvHk8OTw+O/47xjvGO9F7kXufe197dfs1+xR7FHs5uvm66vrq+ul66Xr1evV61LsUuwq7SrtXO5c7vXv9e/v8e/xNvQ29Lf2t/Zf+V/5E/wT/L7+vv5bAVsB5gPmA1IGUganCKcI5QrlCv0M/QzuDu4OuBC4EEQSRBKPE48TnBScFFIVUhW1FbUV0RXRFZ4VnhUsFSwVkRSRFMoTyhPeEt4S1hHWEaQQpBBFD0UPwA3ADRIMEgxBCkEKWwhbCGkGaQZyBHIEhAKEAqsAqwDv/u/+Vf1V/ef75/ul+qX6kvmS+bf4t/gV+BX4p/en93H3cfdi92L3avdq94D3gPeU95T3m/eb95j3mPeJ94n3avdq90f3R/ch9yH3+Pb49tn22fbE9sT2tfa19rT2tPa49rj2u/a79sD2wPbA9sD2tva29qn2qfac9pz2j/aP9pD2kPam9qb20vbS9iD3IPeS95L3Kvgq+O/47/jh+eH5/Pr8+jn8OfyS/ZL9/P78/m0AbQDkAeQBXgNeA9ME0wRGBkYGuAe4BxoJGglrCmsKqguqC8EMwQypDakNXg5eDssOyw7uDu4OzQ7NDmIOYg61DbUN0QzRDLYLtgtrCmsK/Aj8CG4HbgfHBccFEwQTBFoCWgKcAJwA3f7d/if9J/15+3n72PnY+VL4Uvjr9uv2p/Wn9Zj0mPS/87/zHvMe88HywfKq8qry0PLQ8jnzOfPa89rzofSh9IT1hPV19nX2YPdg90L4QvgV+RX50/nT+YD6gPob+xv7ofuh+xL8Evxs/Gz8qfyp/Mj8yPzD/MP8lPyU/EH8QfzF+8X7I/sj+2b6ZvqT+ZP5r/iv+Mz3zPfx9vH2I/Yj9nX1dfXs9Oz0iPSI9Ff0V/Re9F70m/Sb9Br1GvXf9d/16Pbo9jb4NvjN+c35o/uj+679rv3q/+r/SwJLArgEuAQsBywHngmeCfQL9AssDiwOQxBDEB4SHhK7E7sTGxUbFSgWKBbjFuMWVBdUF2cXZxcaFxoXcBZwFlYVVhXAE8ATqhGqEQYPBg+6C7oLvge+BxUDFQOu/a79lveW9wbxBvEV6hXq9eL14hDcENyT1ZPVqc+pz7LKssq3xrfGpMOkw53BncFywHLA37/fv+6/7r9vwG/AL8EvwVTCVMLaw9rDu8W7xTPIM8hcy1zLL88vz8jTyNMc2RzZ+9773kTlROXP68/raPJo8uL44vgw/zD/QgVCBf0K/QpwEHAQpBWkFXcadxryHvIeEiMSI58mnyaNKY0p2yvbK1gtWC0PLg8uIS4hLoQthC1nLGcsBSsFK2gpaCm5J7knJCYkJpskmyQlIyUjziHOIXMgcyAQHxAfsB2wHT0cPRzGGsYaYhliGQ0YDRjYFtgW1BXUFfIU8hQ1FDUUnxOfExoTGhOlEqUSRhJGEuwR7BGcEZwRXxFfESQRJBHqEOoQrRCtEFMQUxDID8gP/Q79DtgN2A1JDEkMUwpTCvwH/AdRBVEFbQJtAnL/cv9z/HP8hvmG+cb2xvYz9DP0yfHJ8ZXvle+M7Yzto+uj6/Tp9Ol/6H/oSOdI53DmcOYA5gDm9eX15WbmZuZN503nmOiY6EXqRepC7ELsdu527tbw1vBf81/zBvYG9s34zfi7+7v70f7R/gQCBAJVBVUFuAi4CA0MDQxHD0cPVRJVEg4VDhVoF2gXYxljGeIa4hrxG/EbohyiHOoc6hzeHN4clxyXHA0cDRxNG00bbRptGl4ZXhkoGCgY2xbbFmoVahXfE98TRxJHEqEQoRD0DvQOTQ1NDakLqQsJCgkKcQhxCN8G3wZQBVAFyQPJA08CTwLkAOQAjv+O/1b+Vv48/Tz9P/w//GX7Zful+qX6+Pn4+V75XvnL+Mv4NPg0+Jz3nPf79vv2SfZJ9pH1kfXO9M70/vP+8zDzMPNi8mLykvGS8dTw1PAn8Cfwi++L7xbvFu/K7srupu6m7rjuuO767vruXe9d7+Tv5O+E8ITwMfEx8fXx9fHW8tby1/PX8wf1B/Vx9nH2FfgV+O757vn3+/f7Hf4d/kgASABqAmoCdAR0BFQGVAYHCAcIkQmRCecK5woQDBAMEw0TDecN5w2QDpAOEw8TD2EPYQ94D3gPXA9cDwUPBQ97DnsO0Q3RDQ4NDg0+DD4McgtyC6QKpArVCdUJBAkECSwILAhJB0kHXQZdBmYFZgVmBGYEYgNiA2ECYQJoAWgBgACAAK7/rv/0/vT+Uv5S/s39zf1k/WT9Gf0Z/e387fze/N785Pzk/Pn8+fwT/RP9J/0n/TL9Mv0r/Sv9Cf0J/cz8zPxt/G386Pvo+0f7R/uL+ov6u/m7+eb45vgU+BT4SPdI95P2k/b59fn1evV69SP1I/Xy9PL04/Tj9Pn0+fQx9TH1gPWA9en16fVp9mn2+fb59qD3oPdi+GL4QPlA+T76Pvpf+1/7oPyg/Pv9+/1s/2z/6wDrAG4CbgLvA+8DawVrBdIG0gYgCCAIUglSCVcKVwosCywL0wvTCz8MPwxzDHMMdQx1DEEMQQzbC9sLSwtLC5AKkAqsCawJpwinCIIHggc+Bj4G5QTlBH4DfgMOAg4CnQCdADf/N//g/eD9nvye/Hr7evt1+nX6jfmN+cn4yfgj+CP4kfeR9xr3Gve69rr2cfZx9kn2SfZG9kb2YvZi9qL2ovb79vv2YPdg9873zvc7+Dv4mvia+Ov46/gm+Sb5RflF+U35Tfk/+T/5G/kb+e347fi0+LT4cfhx+Cv4K/jj9+P3lveW91D3UPcO9w73z/bP9p72nvZ99n32bfZt9nv2e/ao9qj28Pbw9ln3Wffg9+D3g/iD+Eb5Rvkv+i/6P/s/+3j8ePze/d79b/9v/yEBIQHyAvIC2QTZBMAGwAabCJsIYQphCvgL+AtcDVwNiQ6JDm0PbQ8JEAkQYxBjEHEQcRA8EDwQ1A/UDzYPNg9mDmYOag1qDTcMNwzECsQKCQkJCf0G/QaHBIcElwGXAS3+Lf4w+jD6nfWd9ZrwmvAz6zPrh+WH5e/f79+T2pPamtWa1V/RX9H1zfXNWctZy7XJtcnnyOfIuMi4yCzJLMkSyhLKMcsxy5bMlswtzi3O3s/ez9LR0tEZ1BnUr9av1rrZutk93T3dIOEg4VnlWeXR6dHpX+5f7u3y7fJu92732PvY+yEAIQBZBFkEhgiGCJoMmgycEJwQixSLFDsYOxikG6QbuR65HkghSCFNI00j0iTSJMElwSU8JjwmdCZ0JmsmayZPJk8mTCZMJlEmUSZtJm0mrSatJuMm4yYJJwknICcgJ/0m/SalJqUmLCYsJn8lfyWtJK0kzSPNI8cixyKdIZ0hViBWINce1x4cHRwdMRsxGwkZCRmqFqoWLBQsFJMRkxHxDvEOWwxbDNgJ2AllB2UHAQUBBaMCowI2ADYArf2t/Qr7CvtF+EX4ZvVm9Y/yj/LV79XvTe1N7SXrJetm6WbpD+gP6DTnNOfF5sXmruau5vTm9OaJ54nnWehZ6G7pbunE6sTqVuxW7C3uLe5Q8FDwtPK08lX1VfUm+Cb4EfsR+/v9+/3TANMAhgOGA/0F/QU5CDkIPAo8Cv8L/wuUDZQNCA8ID1IQUhB6EXoRhhKGEmETYRMMFAwUihSKFMUUxRS/FL8UfxR/FPoT+hM3EzcTSxJLEjgROBEPEA8Q6Q7pDssNyw3CDMIM2wvbCxILEgtoCmgK3wnfCW0JbQkQCRAJygjKCJIIkghmCGYIQwhDCB4IHgjzB/MHvwe/B30HfQcqByoHyAbIBlIGUgbGBcYFIwUjBWcEZwSPA48DmwKbAo8BjwFoAGgALf8t/+T95P2O/I78Kvsq+8L5wvlO+E74zPbM9kj1SPXB88HzNPI08rbwtvBJ70nv6+3r7brsuuy667rr7+rv6nLqcupD6kPqV+pX6rTqtOpN603rD+wP7P/s/+wc7hzuYu9i797w3vCb8pvymPSY9Nf21/ZR+VH58vvy+5/+n/5DAUMByQPJAxcGFwYpCCkIAQoBCpQLlAvuDO4MHQ4dDhkPGQ/vD+8PqRCpEDcRNxGZEZkR0hHSEc0RzRGMEYwRGxEbEXYQdhCtD60P2A7YDvcN9w0UDRQNNAw0DE4LTgtdCl0KYQlhCVIIUggwBzAH/gX+BcEEwQR8A3wDOgI6AgMBAwHe/97/0f7R/uD94P0N/Q39WPxY/Mb7xvtX+1f7BfsF+9D60Pqt+q36k/qT+n76fvpq+mr6VPpU+kD6QPon+if6A/oD+tT51PmW+Zb5RflF+e347fiT+JP4Ovg6+O337few97D3hfeF93X3dfeD94P3rPes9/T39PdX+Ff4z/jP+Fn5Wfn0+fT5mfqZ+kb7Rvv7+/v7tfy1/Hb9dv1D/kP+Hf8d/wAAAADrAOsA2QHZAcACwAKeA54DcgRyBDcFNwXuBe4FlgaWBiQHJAeXB5cH7AfsBxwIHAgpCCkIGQgZCOkH6QegB6AHRAdEB9YG1gZYBlgGzAXMBTEFMQWCBIIEwwPDA/MC8wIVAhUCLQEtAUEAQQBV/1X/bP5s/o/9j/3C/ML8CfwJ/Gz7bPvp+un6evp6+iH6IfrT+dP5ivmK+Un5SfkN+Q351/jX+Kv4q/iK+Ir4bPhs+Fj4WPhJ+En4Nvg2+CX4JfgP+A/46vfq97r3uvd793v3Kfcp98/2z/Zy9nL2E/YT9sL1wvWE9YT1WPVY9Ur1SvVZ9Vn1fPV89bj1uPUG9gb2X/Zf9sn2yfZH90f32ffZ94b4hvhQ+VD5Nfo1+jT7NPtO/E78gf2B/cr+yv4qACoAoQGhASMDIwOxBLEERwZHBtQH1AdUCVQJvwq/Cv8L/wsNDQ0N5w3nDXwOfA7PDs8O5g7mDrgOuA5NDk0OsQ2xDeQM5AzsC+wL0grSCpUJlQksCCwIlQaVBsoEygS6AroCWABYAKT9pP2D+oP65fbl9t7y3vJj7mPugemB6XrkeuRq32rfedp52gLWAtYn0ifS+877zrzMvMxiy2LLxcrFyvHK8cq1y7XL0MzQzETORM7sz+zPo9Gj0YXThdOW1ZbV0tfS12HaYdpQ3VDdk+CT4DHkMeQX6BfoH+wf7DHwMfA39Df0Hvge+Nr72vt3/3f//gL+Am8GbwbfCd8JVw1XDcEQwRAcFBwUWxdbF0saSxrYHNgc+B74HoAggCB+IX4hEiISIj4iPiIzIjMiKCIoIiIiIiJCIkIipSKlIicjJyPEI8QjcSRxJPgk+CRLJUslcSVxJU0lTSXzJPMkhCSEJPQj9CNVI1UjvCK8IgwiDCJAIUAhViBWICkfKR+pHakd2xvbG60ZrRkrFysXdRR1FJoRmhG0DrQO3AvcCx4JHgl1BnUG3QPdA0wBTAGt/q3+9Pv0+yj5KPlH9kf2XvNe85Pwk/D67frtoOug667prukp6CnoCucK52fmZ+Yx5jHmUeZR5srmyuaK54rnfOh86Kfpp+kM6wzrpeyl7H7ufu6d8J3w+fL58o/1j/VV+FX4N/s3+x3+Hf72APYArwOvAzAGMAZ2CHYIgwqDCk0MTQzeDd4NRg9GD3sQexCNEY0RiBKIEl8TXxMVFBUUrRStFBEVERU8FTwVMhUyFeMU4xRXFFcUnxOfE7sSuxK9Eb0RuxC7ELQPtA+xDrEOvA28DcwMzAzkC+QLCQsJCzUKNQpnCWcJogiiCOAH4AcfBx8HYAZgBqIFogXlBOUELQQtBHkDeQPHAscCFgIWAmUBZQGtAK0A7f/t/yT/JP9S/lL+ev16/aX8pfzW+9b7DvsO+1L6Uvqa+Zr53vje+CL4Ivhe9173jvaO9rz1vPXm9Ob0CPQI9DXzNfNw8nDyvvG+8TTxNPHT8NPwlPCU8H7wfvCF8IXwl/CX8Lfwt/Df8N/wCfEJ8UrxSvGt8a3xPfI98g/zD/Mr9Cv0h/WH9Rn3GffQ+ND4kvqS+k38Tfz5/fn9j/+P/woBCgFyAnICywPLAxUFFQVaBloGnwefB9gI2AgGCgYKIgsiCxYMFgzbDNsMcA1wDcgNyA3sDewN8A3wDdgN2A23DbcNnw2fDYkNiQ10DXQNXQ1dDTENMQ3qDOoMiAyIDP8L/wtNC00LdAp0CnIJcglICEgIAwcDB6sFqwVJBEkE6QLpApcBlwFaAFoAPf89/0n+Sf6A/YD93/zf/GL8YvwA/AD8svuy+3j7ePtM+0z7Kfsp+wr7Cvvn+uf6tfq1+nj6ePow+jD64Png+Zf5l/lX+Vf5Ifkh+fz4/Pjr+Ov47Pjs+Af5B/k6+Tr5gvmC+eD54PlR+lH6z/rP+lj7WPvo++j7efx5/Az9DP2f/Z/9M/4z/sj+yP5e/17/8v/y/4AAgAANAQ0BmwGbAS0CLQLKAsoCdAN0AyYEJgTbBNsEjgWOBTEGMQa/Br8GMwczB4MHgwexB7EHwgfCB7cHtweXB5cHaQdpBycHJwfNBs0GWwZbBssFywUdBR0FVQRVBHcDdwOHAocCigGKAYgAiACI/4j/lf6V/rn9uf33/Pf8TfxN/Ln7ufsz+zP7sfqx+jP6M/q0+bT5M/kz+br4uvhJ+En43/ff94X3hfc29zb36fbp9qL2ovZc9lz2D/YP9sT1xPV59Xn1J/Un9dv02/SV9JX0UvRS9CP0I/QI9Aj0/fP98w30DfQ29Db0cvRy9Mj0yPQ39Tf1t/W39Ur2Svbw9vD2ovei92b4Zvg++T75Jfol+hz7HPsf/B/8K/0r/T7+Pv5e/17/jgCOAMsBywEXAxcDbQRtBMEFwQUNBw0HSwhLCGoJagliCmIKMQsxC8YLxgsjDCMMTQxNDD4MPgz6C/oLjAuMC/EK8QoxCjEKWQlZCWgIaAheB14HOwY7BvgE+ASFA4UD2gHaAfL/8v+y/bL9BvsG+/D38PdW9Fb0NfA18Lvru+v85vzmG+Ib4nbddt032TfZhdWF1bDSsNLD0MPQrM+sz3vPe88B0AHQ+9D70FvSW9Lu0+7ThNWE1SfXJ9fO2M7Yc9pz2j/cP9xK3kreleCV4DnjOeMs5izmSulK6X3sfeyq76rvtPK08pb1lvVY+Fj4BvsG+679rv1tAG0AVgNWA2cGZwakCaQJ/gz+DEIQQhBSE1ITERYRFk0YTRgBGgEaQBtAGwccBxyCHIIc6RzpHEwdTB3aHdodtx63Hsofyh8UIRQhiSKJIucj5yMaJRolGCYYJrMmsyb3JvcmAicCJ8YmxiZfJl8m7CXsJVclVyWjJKMk1CPUI8cixyJvIW8h0R/RH9Ed0R1zG3MbyxjLGOMV4xXVEtUSyQ/JD9UM1QwJCgkKbwdvBwIFAgWxArECawBrACj+KP7a+9r7ffl9+SX3JffZ9Nn0pvKm8rHwsfAE7wTvp+2n7a/sr+wW7Bbsz+vP69zr3Oss7Czsqeyp7FPtU+0e7h7u/u7+7vvv++8V8RXxSfJJ8qDzoPMd9R31ufa59nL4cvg++j76DPwM/M/9z/1+/37/FgEWAZQClAL/A/8DXAVcBaUGpQbdB90HCQkJCSIKIgoxCzELPww/DD4NPg0tDi0OBg8GD7MPsw8xEDEQiRCJEK4QrhCqEKoQixCLEEsQSxD2D/YPmw+bDzMPMw/GDsYOWQ5ZDt8N3w1XDVcNxgzGDCUMJQx0C3QLtwq3CugJ6AkECQQJEAgQCBAHEAcLBgsGDQUNBRwEHAQ3AzcDYAJgApIBkgHKAMoABwAHAEz/TP+Y/pj+7f3t/VD9UP28/Lz8LPws/KD7oPsO+w77bvpu+sX5xfkO+Q75SfhJ+H73fveu9q721/XX9Qn1CfVJ9En0mfOZ8wzzDPOf8p/ySvJK8g3yDfLY8djxmfGZ8VfxV/ES8RLxzfDN8KbwpvCx8LHw+fD58JLxkvF/8n/ysfOx8x31HfWw9rD2UPhQ+PD58PmG+4b7DP0M/X3+ff7g/+D/NQE1AXsCewK1A7UD6AToBA0GDQYkByQHKwgrCA0JDQnACcAJQwpDCowKjAqvCq8KxQrFCtkK2Qr7CvsKMAswC2YLZguTC5MLtgu2C8ILwgu4C7gLnAucC2ALYAv+Cv4Kewp7CtIJ0gkOCQ4JPgg+CGUHZQeLBosGtQW1BegE6AQtBC0EjgOOAw8DDwOnAqcCTgJOAvUB9QGVAZUBLQEtAcAAwABLAEsAzP/M/zv/O/+Q/pD+y/3L/fr8+vwo/Cj8Xftd+6f6p/oE+gT6cPlw+fD48PiG+Ib4Nfg1+Av4C/gM+Az4NPg0+If4h/j8+Pz4ivmK+Sz6LPrc+tz6mPuY+2D8YPw5/Tn9Iv4i/hb/Fv8TABMAFAEUARICEgIOAw4DCwQLBP8E/wTmBeYFuga6Bm0HbQf4B/gHWghaCIsIiwiNCI0IZQhlCBUIFQimB6YHJgcmB5sGmwYKBgoGdwV3BdsE2wQxBDEEdwN3A64CrgLWAdYB9AD0AA8ADwAp/yn/SP5I/nf9d/28/Lz8Hfwd/KT7pPtM+0z7DvsO++P64/q8+rz6jvqO+lj6WPob+hv62/nb+aP5o/l0+XT5RvlG+Rv5G/no+Oj4qPio+GX4Zfgb+Bv4yPfI93H3cfcP9w/3nPac9if2J/a09bT1RvVG9er06vSe9J70XPRc9C70LvQV9BX0EfQR9DD0MPRw9HD0y/TL9Ef1R/Xj9eP1m/ab9nP3c/dn+Gf4bflt+Xz6fPqR+5H7qfyp/Mf9x/3x/vH+KwArAG8BbwG/Ar8CGQQZBG8FbwW+Br4G/Qf9BxcJFwkBCgEKuAq4CisLKwtaC1oLSQtJC/YK9gpnCmcKrQmtCdMI0wjqB+oHAgcCBx0GHQY2BTYFQwRDBDoDOgMGAgYCmQCZAOf+5/7P/M/8Ofo5+if3J/eK84rza+9r7wXrBet45njm8eHx4cvdy90w2jDaOtc61yfVJ9Xz0/PTfNN808bTxtOc1JzUutW61RbXFteG2IbY49nj2UPbQ9ur3KvcHd4d3sXfxd+x4bHh2OPY40bmRubl6OXoj+uP6zDuMO6v8K/w9vL28gv1C/UD9wP39fj1+AH7AftI/Uj92P/Y/6cCpwKxBbEF3QjdCP4L/gv4DvgOrRGtEfET8RO+Fb4VJBckFyUYJRjuGO4YtBm0GYAagBp2G3YbrxyvHAweDB6MH4wfJiEmIZ4iniLlI+Uj9ST1JKglqCUMJgwmQiZCJjgmOCYCJgImsyWzJSwlLCVtJG0kgSOBI0siSyLJIMkgCB8IH/Yc9hybGpsaFhgWGHUVdRXUEtQSVhBWEAQOBA7jC+ML8wnzCSUIJQhkBmQGowSjBNQC1ALuAO4A8/7z/vT89Pz5+vn6EvkS+VX3VffI9cj1b/Rv9FzzXPON8o3y+/H78avxq/GS8ZLxnvGe8dHx0fEh8iHygfKB8vry+vKL84vzL/Qv9O/07/TL9cv1u/a79rz3vPfF+MX4zfnN+dX61frg++D78vzy/Az+DP4x/zH/XABcAIgBiAG1ArUC5APkAw8FDwU2BjYGVAdUB1sIWwhECUQJDQoNCqYKpgoOCw4LTAtMC1gLWAtAC0ALFQsVC9oK2gqbCpsKXwpfCh4KHgreCd4JqAmoCXsJewlhCWEJXgleCWEJYQliCWIJWwlbCT4JPgkQCRAJ2AjYCJYIlghNCE0IAggCCK4HrgdUB1QH9Qb1Bo0GjQYcBhwGpAWkBSMFIwWZBJkEBQQFBGQDZAOrAqsC1gHWAeMA4wDS/9L/qv6q/nj9eP0//D/8AfsB+835zfmh+KH4g/eD94L2gvaa9Zr1xfTF9Af0B/RW81bzp/Kn8gjyCPJ28Xbx8/Dz8JXwlfBn8GfwdvB28Nvw2/Cb8ZvxqfKp8vXz9fNf9V/1wfbB9gr4Cvgs+Sz5JPok+vv6+/q1+7X7VPxU/Nz83PxV/VX9x/3H/UD+QP7J/sn+ZP9k/wMAAwCXAJcAEAEQAWUBZQGhAaEB1wHXARwCHAKDAoMCEAMQA7cDtwNyBHIEOwU7BQoGCgbgBuAGuQe5B4EIgQgqCSoJrAmsCf8J/wkyCjIKVgpWCmkKaQpqCmoKVgpWCiEKIQrUCdQJiAmICUQJRAkPCQ8J4QjhCKIIoghECEQIzQfNB0MHQwezBrMGIwYjBoQFhAXDBMME1gPWA8ICwgKaAZoBdwB3AGz/bP92/nb+jv2O/az8rPzP+8/7/vr++kr6Svq8+bz5VflV+Rb5Fvn3+Pf48/jz+A75DvlI+Uj5nvme+RD6EPqa+pr6M/sz+9r72vuN/I38S/1L/RL+Ev7l/uX+wf/B/6AAoACBAYEBYgJiAjoDOgMLBAsE1ATUBIoFigUqBioGsQaxBhYHFgdbB1sHigeKB6UHpQeuB64HpgemB4IHggc9Bz0H2AbYBlYGVgbCBcIFJgUmBYIEggTRA9EDEQMRA0ACQAJiAWIBgQCBAKv/q//o/uj+Ov46/qD9oP0R/RH9gvyC/PT79Ptk+2T71PrU+k76TvrQ+dD5V/lX+eT45Ph0+HT4BvgG+Kf3p/dc91z3I/cj9/72/vbh9uH2vva+9pn2mfZt9m32N/Y39v/1//W/9b/1cfVx9SH1IfXZ9Nn0oPSg9Ij0iPSP9I/0pvSm9Mv0y/T59Pn0MfUx9YX1hfX49fj1gPaA9hf3F/ey97L3SfhJ+On46fij+aP5gfqB+of7h/uw/LD87/3v/Tj/OP+GAIYA1QHVARgDGANEBEQETAVMBSEGIQa+Br4GKAcoB1sHWwddB10HOAc4B/EG8QaYBpgGQwZDBvkF+QW+Bb4FjQWNBVUFVQUCBQIFhwSHBNkD2QPfAt8CggGCAa//r/9F/UX9NPo0+pb2lvZ58nnyAu4C7oTphOku5S7lNOE04end6d1s22zbwdnB2QLZAtkL2QvZodmh2azarNrz2/PbN9033XHecd6A34DfTeBN4Pjg+OCQ4ZDhKuIq4v7i/uIg5CDkjOWM5UTnROcp6SnpDesN6+Ts5Oyh7qHuRfBF8Ofx5/Gf85/zefV59YT3hPfQ+dD5Y/xj/Dr/Ov9UAlQCmQWZBdMI0wjfC98Lnw6fDvMQ8xDqEuoSpBSkFCYWJhaSF5IXBhkGGXYadhr1G/UbkR2RHSEfIR+SIJIg0CHQIaEioSIEIwQjGCMYI94i3iKBIoEiKiIqIskhySFrIWshISEhIdUg1SCNII0gUyBTIPgf+B9mH2YfkR6RHlgdWB3EG8Qb9xn3Gf0X/RfyFfIV8hPyE/8R/xEdEB0QVg5WDqEMoQz9Cv0KbAlsCeYH5gdnBmcG7wTvBHkDeQP7AfsBdAB0AOj+6P5Y/Vj9y/vL+0/6T/rj+OP4hfeF9z/2P/YW9Rb1EPQQ9EXzRfO/8r/yfvJ+8ozyjPLm8ubyhPOE82f0Z/SF9YX1yfbJ9iD4IPh1+XX5t/q3+uL74vv3/Pf89P30/df+1/6a/5r/NwA3AK8ArwAMAQwBYQFhAbwBvAEnAicCpgKmAi4DLgO6A7oDRgRGBMsEywRKBUoFwQXBBSYGJgZsBmwGjgaOBoEGgQZPBk8GDAYMBsgFyAWbBZsFkwWTBa0FrQXnBecFOwY7Bp8GnwYSBxIHlAeUBxUIFQiLCIsI6QjpCB8JHwktCS0JIQkhCf8I/wjQCNAImQiZCFIIUgj3B/cHiQeJBwUHBQduBm4GwgXCBfwE/AQZBBkEHgMeAxkCGQIYARgBLQAtAGP/Y/+0/rT+Fv4W/oD9gP3g/OD8LPws/GP7Y/t/+n/6gPmA+Xb4dvhu9273ffZ99sX1xfVd9V31TPVM9Zf1l/Um9ib20/bT9nz3fPf59/n3L/gv+CH4IfjX99f3X/df99X21fZL9kv2yvXK9Wz1bPVE9UT1XvVe9cv1y/WB9oH2Zfdl91v4W/hM+Uz5Kfop+v36/frX+9f7w/zD/MD9wP3I/sj+zv/O/8kAyQC5AbkBoQKhAnkDeQM5BDkE1ATUBD4FPgV7BXsFnQWdBbQFtAXOBc4F8AXwBREGEQYqBioGQAZABlQGVAZxBnEGmQaZBr0GvQbPBs8GyAbIBqUGpQZ7BnsGZgZmBnEGcQaeBp4G4gbiBiMHIwdRB1EHbAdsB3EHcQdlB2UHRgdGBwIHAgeFBoUGyAXIBc0EzQSnA6cDdAJ0AksBSwE8ADwASv9K/3f+d/7C/cL9Lf0t/b/8v/x6/Hr8WPxY/FH8Ufxa/Fr8bfxt/I38jfzC/ML8EP0Q/Xb9dv3u/e79av5q/uX+5f5e/17/1//X/1EAUQDGAMYAKwErAXUBdQGjAaMBwgHCAeQB5AEZAhkCaQJpAsYCxgIfAx8DZQNlA48DjwOjA6MDqQOpA6UDpQOSA5IDZwNnAxsDGwOxArECOQI5AsUBxQFqAWoBMAEwARYBFgEYARgBLQEtAU0BTQFwAXABjwGPAZ0BnQGMAYwBUgFSAe4A7gBfAF8Arf+t/+P+4/4H/gf+Hv0e/TT8NPxK+0r7Zfpl+o/5j/nD+MP4/Pf89zz3PPd79nv2tfW19fj0+PRN9E30uvO681LzUvMR8xHz6/Lr8t/y3/Lk8uTy9PL08h7zHvNn82fzzPPM81D0UPTm9Ob0gPWA9SH2IfbO9s72ifeJ91n4Wfg6+Tr5Hvoe+v/6//rZ+9n7rvyu/IL9gv1a/lr+Mf8x//7//v++AL4AbAFsAQQCBAKGAoYC7gLuAjADMANLA0sDQQNBAxwDHAPtAu0CwALAApoCmgJ6AnoCXQJdAkMCQwItAi0CHQIdAgoCCgLZAdkBZAFkAYoAigAd/x3/CP0I/Vb6VvoL9wv3Q/ND80HvQe806zTrVudW5wTkBORy4XLhu9+73wPfA98t3y3f/N/830XhReG64rriD+QP5CnlKeXh5eHlI+Yj5hDmEObD5cPlXuVe5SvlK+VP5U/l0uXS5cDmwOb05/TnOuk66XzqfOqk66Trq+yr7KjtqO2u7q7uyO/I7xTxFPGv8q/ysfSx9C33Lfcl+iX6df11/dkA2QAXBBcE/gb+Bm8Jbwl+C34LTg1ODe0O7Q5+EH4QHBIcEsETwROHFYcVehd6F3YZdhlcG1wbBB0EHSYeJh60HrQexB7EHmYeZh7UHdQdSR1JHc8czxyJHIkcmRyZHPIc8hyiHaIdqB6oHsMfwx/KIMogmSGZIfQh9CHWIdYhWSFZIXsgeyBWH1YfBh4GHn8cfxzLGssa+xj7GA4XDhcQFRAVFhMWExkRGREXDxcPFg0WDRMLEwsWCRYJNQc1B3sFewXuA+4DjAKMAkkBSQETABMA4v7i/rv9u/2f/J/8kfuR+5j6mPqu+a750vjS+BX4FfiJ94n3Pvc+90X3RfeX95f3Ifgh+M/4z/iS+ZL5W/pb+ib7Jvvm++b7hfyF/O387fwK/Qr92PzY/Gj8aPzT+9P7M/sz+6b6pvo++j76CPoI+g/6D/pd+l368/rz+sz7zPvd/N38D/4P/kn/Sf93AHcAiAGIAW8CbwIsAywDxgPGA0EEQQSrBKsEDQUNBWcFZwW9Bb0FEQYRBl4GXgahBqEG1gbWBu4G7gbnBucGyAbIBpcGlwZpBmkGUAZQBkoGSgZSBlIGXQZdBlwGXAZNBk0GMwYzBg0GDQbWBdYFiAWIBRsFGwWUBJQEBwQHBIoDigMuAy4D+AL4AtcC1wKwArACagJqAvYB9gFOAU4BdwB3AH//f/94/nj+ef15/Z/8n/wC/AL8rvuu+6f7p/vZ+9n7Jvwm/HP8c/yl/KX8rPys/IX8hfwt/C38pvum+/76/vpE+kT6i/mL+fP48/iR+JH4bfht+If4h/jK+Mr4F/kX+Vr5WvmE+YT5lfmV+aL5ovm6+br54/nj+SH6Ifpm+mb6p/qn+uH64foa+xr7VftV+5n7mfvf+9/7G/wb/Er8Svxu/G78k/yT/Mz8zPwm/Sb9o/2j/UD+QP72/vb+wf/B/5gAmABzAXMBRAJEAvUC9QJ/A38D4wPjAysEKwRrBGsEsASwBPUE9QQ0BTQFZgVmBYcFhwWjBaMFyQXJBfgF+AUoBigGRAZEBjMGMwboBegFZQVlBbcEtwTuA+4DGwMbA0oCSgKDAYMB0QDRADwAPADO/87/i/+L/3P/c/+D/4P/sf+x//D/8P8zADMAbwBvAJsAmwC4ALgAyQDJANkA2QDvAO8AEQERAT4BPgFxAXEBpAGkAdEB0QH0AfQBDQINAh0CHQIqAioCPgI+Al0CXQKMAowCyALIAgYDBgNAA0ADcwNzA54DngPFA8UD6QPpAwEEAQQABAAE4APgA58DnwNGA0YD6ALoApACkAJDAkMCAQIBAsEBwQGBAYEBQQFBAQMBAwHIAMgAjQCNAE0ATQAEAAQAsP+w/1b/Vv/4/vj+mf6Z/jz+PP7j/eP9jv2O/UT9RP0E/QT9x/zH/If8h/w3/Df8zvvO+1H7UfvC+sL6KPoo+o75jvnz+PP4U/hT+LH3sfcL9wv3X/Zf9r71vvUq9Sr1pPSk9Dv0O/Tt8+3ztfO185zznPOh86Hzv/O/8wT0BPRu9G709/T39J71nvVY9lj2Gvca9+T35Pe3+Lf4lPmU+Xv6e/pk+2T7QfxB/AX9Bf2o/aj9Jf4l/oH+gf68/rz+1/7X/tX+1f67/rv+l/6X/nn+ef5z/nP+kf6R/tX+1f49/z3/w//D/14AXgAJAQkBtQG1AVACUAK5ArkC0wLTAoECgQKlAaUBLAAsABv+G/5r+2v7L/gv+Jz0nPTo8OjwVe1V7UTqROr25/bniuaK5h/mH+aX5pfmrueu5y7pLunA6sDqD+wP7PTs9OxA7UDt3Ozc7Obr5ut+6n7q1+jX6FLnUucx5jHmmeWZ5avlq+VG5kbmKecp5yvoK+gf6R/p6Ono6Z/qn+pX61frH+wf7BvtG+1n7mfuE/AT8DjyOPLZ9Nn01PfU9/D68Prp/en9fgB+AIsCiwIVBBUEOgU6BR8GHwb4BvgG8AfwBx8JHwmlCqUKjwyPDL0OvQ4JEQkRNBM0E+oU6hQCFgIWfxZ/Fm0WbRYPFg8WrhWuFW4VbhWAFYAVBxYHFvsW+xZpGGkYTBpMGmYcZhyIHogegiCCIP8h/yHoIugiRiNGIxIjEiN0InQimSGZIX8gfyAyHzIfwx3DHSEcIRxVGlUadxh3GIYWhhaQFJAUqhKqEtQQ1BAkDyQPuA24DZsMmwzbC9sLdgt2C0oLSgs2CzYLHQsdC+EK4QpyCnIKzwnPCe0I7QjIB8gHZwZnBt0E3QRKA0oD1QHVAaAAoAC4/7j/Gf8Z/7T+tP50/nT+S/5L/jP+M/4e/h7+//3//cb9xv1h/WH9yfzJ/Av8C/w7+zv7b/pv+sP5w/lD+UP58vjy+Nj42Pj2+Pb4R/lH+cn5yflw+nD6J/sn+9z73Pt5/Hn88Pzw/D79Pv1m/Wb9bf1t/V79Xv09/T39Dv0O/dX81fyY/Jj8W/xb/Cv8K/wR/BH8EfwR/DL8Mvx1/HX84Pzg/Hb9dv1A/kD+Pf89/2AAYACYAZgBzgLOAukD6QPcBNwEoQWhBTAGMAaDBoMGmwabBnUGdQYaBhoGmwWbBQYFBgVrBGsE0QPRAzoDOgOqAqoCKgIqAskByQGWAZYBnAGcAdIB0gEqAioCjQKNAuYC5gImAyYDSANIA0kDSQMqAyoD7ALsAo8CjwIXAhcCjAGMAfwA/AB5AHkAEAAQAMr/yv+f/5//gv+C/17/Xv8h/yH/wv7C/kf+R/65/bn9JP0k/ZP8k/wF/AX8dvt2++367fpw+nD6BvoG+rj5uPmD+YP5XPlc+UP5Q/k1+TX5M/kz+Ur5Svl6+Xr5vvm++RL6Evpx+nH61vrW+kj7SPvC+8L7NPw0/I/8j/zD/MP8zfzN/L/8v/yy/LL8vvy+/O387fw4/Tj9kP2Q/ez97P1P/k/+v/6//kT/RP/b/9v/dAB0APkA+QBdAV0BngGeAcoBygHxAfEBJAIkAmQCZAKvAq8C/AL8AkIDQgN9A30DqgOqA8MDwwPEA8QDrQOtA3oDegMtAy0DzQLNAl4CXgLmAeYBbwFvAf4A/gCbAJsATgBOABsAGwAEAAQABwAHAB0AHQBEAEQAeQB5ALwAvAAUARQBhgGGARACEAKuAq4CVgNWA/QD9AOABIAE9wT3BFYFVgWgBaAF1wXXBfIF8gXqBeoFvwW/BXEFcQUNBQ0FpASkBEMEQwT0A/QDvAO8A5cDlwOFA4UDhgOGA5YDlgO2A7YD4QPhAxAEEAQ5BDkEVwRXBGAEYARNBE0EHgQeBNID0gNsA2wD9AL0Am8CbwLeAd4BQQFBAZgAmADk/+T/Jv8m/2n+af6v/a/9+vz6/Ez8TPyg+6D77/rv+jn6Ofp3+Xf5pPik+Mv3y/fx9vH2GvYa9l31XfW99L30N/Q39NXz1fOV85Xzd/N3843zjfPb89vzVfRV9PT09PSa9Zr1LPYs9qT2pPYC9wL3TvdO9533nffx9/H3Q/hD+JP4k/jZ+Nn4E/kT+U35TfmP+Y/52PnY+S76LvqL+ov66vrq+kr7Svup+6n7BPwE/GD8YPy+/L78If0h/Y79jv0B/gH+cv5y/tL+0v4Q/xD/HP8c//P+8/6K/or+2/3b/eH84fyD+4P7r/mv+Wz3bPfH9Mf05vHm8R7vHu+57Lns+er56iHqIepD6kPqQutC6/ns+ewe7x7vWfFZ8WfzZ/P+9P704/Xj9QD2APZD9UP1vPO887DxsPFp72nvPO087YzrjOuG6obqKOoo6l/qX+rs6uzqheuF6w3sDexx7HHssuyy7Pns+exl7WXtCu4K7gTvBO9c8FzwB/IH8vrz+vMQ9hD2E/gT+NP50/kj+yP77vvu+0D8QPw//D/8I/wj/C38Lfyd/J38nf2d/T3/Pf94AXgBIAQgBOQG5AZyCXIJhAuEC+YM5gykDaQN7w3vDfsN+w0TDhMOcA5wDiMPIw9HEEcQ6BHoEegT6BM4FjgWsxizGAUbBRv3HPccaR5pHjEfMR9oH2gfQx9DH9Ye1h5NHk0eyB3IHTMdMx2FHIUcuBu4G6kaqRpTGVMZxxfHFwcWBxY2FDYUghKCEgMRAxHaD9oPIA8gD84Ozg7dDt0OPg8+D8APwA8+ED4QkxCTEJIQkhAlECUQUQ9RDxkOGQ6ZDJkMAAsAC24JbgkKCAoI7wbvBh0GHQaNBY0FLwUvBekE6QSuBK4EewR7BE0ETQQkBCQE/wP/A9UD1QOhA6EDZANkAyEDIQPiAuICqwKrAn0CfQJPAk8CFgIWAsoBygFnAWcB8gDyAHQAdAD2//b/fv9+/wz/DP+Y/pj+Gf4Z/oz9jP3u/O78QfxB/JP7k/vo+uj6QPpA+qP5o/kM+Qz5e/h7+Pr3+veP94/3Q/dD9yb3JvdC90L3nfed9z/4P/gk+ST5Ovo6+mv7a/uW/Jb8mP2Y/Vf+V/6//r/+x/7H/nX+df7U/dT9+/z7/BT8FPxM+0z70vrS+sv6y/pH+0f7Nvw2/HX9df3X/tf+LAAsAFIBUgE6AjoC4wLjAlIDUgOVA5UDuwO7A9ID0gPpA+kDEAQQBEwETASdBJ0E/QT9BFUFVQWRBZEFpwWnBZAFkAVbBVsFIwUjBfsE+wT1BPUEFAUUBUQFRAVvBW8FewV7BVMFUwXnBOcENQQ1BEEDQQMZAhkC1wDXAJz/nP+L/ov+w/3D/VX9Vf1E/UT9f/1//fL98v1+/n7+Bv8G/2z/bP+W/5b/cf9x//v++/4//j/+VP1U/WL8YvyC+4L7xfrF+jT6NPrK+cr5gPmA+Vn5WflZ+Vn5gPmA+c75zvk4+jj6rfqt+iT7JPuV+5X7APwA/Gr8avzU/NT8M/0z/YD9gP2p/an9pP2k/Xb9dv0o/Sj9x/zH/Gv8a/wj/CP8/Pv8+//7//sw/DD8jvyO/BT9FP25/bn9cf5x/i3/Lf/d/93/cgByAOEA4QAlASUBQwFDAUUBRQE/AT8BQAFAAVUBVQGGAYYB1gHWAT4CPgK7ArsCRANEA8kDyQM+BD4EmwSbBNcE1wTyBPIE9QT1BOIE4gS/BL8EkASQBFUEVQQRBBEEzgPOA5ADkANeA14DOAM4AxsDGwMGAwYD/gL+AgUDBQMlAyUDYQNhA7ADsAMGBAYEVwRXBJIEkgSzBLMEugS6BKMEowRxBHEEJgQmBMMDwwNSA1ID3gLeAnICcgIXAhcC1gHWAa0BrQGeAZ4BpQGlAboBugHRAdEB3QHdAdEB0QGhAaEBSQFJAckAyQAeAB4ATP9M/1j+WP5J/Un9Kvwq/BD7EPsH+gf6FfkV+UX4RfiO94735fbl9lD2UPbI9cj1SPVI9d303fSG9Ib0QfRB9Bv0G/QQ9BD0GvQa9Dn0OfRj9GP0iPSI9K70rvTT9NP09/T39Cf1J/Vn9Wf1tvW29SH2Ifav9q/2Yvdi90P4Q/hL+Uv5aPpo+oX7hfuJ/In8Xv1e/ff99/1K/kr+Uv5S/hD+EP54/Xj9gfyB/C77Lvt2+Xb5Wvda9//0//SB8oHyEfAR8Pjt+O1v7G/smeuZ65nrmetn7Gfs2u3a7cbvxu/h8eHx2PPY8231bfVa9lr2dPZ09r71vvVI9Ej0RPJE8gzwDPD17fXtRuxG7EbrRusE6wTrYuti60DsQOxh7WHtj+6P7r7vvu/q8OrwGPIY8lvzW/O49Lj0LPYs9rf3t/dV+VX58vry+nP8c/yn/af9WP5Y/mX+Zf7J/cn9p/yn/Eb7Rvv7+fv5D/kP+cH4wfg1+TX5cvpy+mL8YvzS/tL+cwFzAegD6APjBeMFOgc6B+QH5AcHCAcI4QfhB6QHpAeGB4YHrgeuBykIKQgGCQYJQgpCCrcLtwtCDUINwQ7BDgYQBhAVERURBxIHEusS6xLoE+gTGBUYFWkWaRbPF88XMRkxGU0aTRr1GvUaChsKG2YaZhoWGRYZUBdQF0QVRBU9Ez0ThBGEET0QPRCND40Phg+GDwoQChD5EPkQHhIeEioTKhPnE+cTPRQ9FBgUGBSLE4sTuRK5ErERsRGMEIwQZg9mD0MOQw4sDSwNIQwhDBALEAvpCekJqgiqCFUHVQcGBgYG4QThBP4D/gNyA3IDQANAA10DXQO4A7gDNwQ3BLgEuAQVBRUFMgUyBfsE+wRyBHIErwOvA88CzwLyAfIBMwEzAaMAowBIAEgAJgAmADIAMgBfAF8AlwCXAMYAxgDgAOAA4wDjANIA0gCzALMAiwCLAF0AXQAoACgA8f/x/7z/vP+L/4v/Xf9d/zP/M/8N/w3/7/7v/uH+4f7m/ub++v76/g//D/8Q/xD/7/7v/qT+pP4u/i7+j/2P/dT81PwG/Ab8N/s3+4L6gvoA+gD6u/m7+bT5tPnW+db5BPoE+iz6LPpF+kX6UvpS+mH6Yfp6+nr6m/qb+sr6yvoK+wr7Y/tj++L74vuI/Ij8SP1I/Qn+Cf61/rX+Pv8+/6T/pP/8//z/YQBhAOcA5wCdAZ0BhQKFAowDjAOhBKEErAWsBY8GjwYxBzEHhgeGB38HfwciByIHgAaABq8FrwXPBM8EAgQCBFkDWQPgAuACkwKTAmICYgI+Aj4CGAIYAugB6AGnAacBWQFZAf8A/wCfAJ8ARABEAPf/9/+7/7v/if+J/1T/VP8E/wT/hv6G/tz93P0M/Qz9Lfwt/F37Xfuy+rL6Ofo5+v35/fkA+gD6O/o7+qj6qPo3+zf70vvS+2P8Y/zT/NP8E/0T/SP9I/0H/Qf9z/zP/I78jvxV/FX8KPwo/Aj8CPzu++770PvQ+7L7svuZ+5n7jfuN+5n7mfu7+7v76vvq+x78HvxR/FH8gfyB/LP8s/zm/Ob8Ev0S/TD9MP06/Tr9M/0z/Sv9K/04/Tj9Z/1n/b79vv02/jb+xf7F/mH/Yf8DAAMApwCnAEMBQwHNAc0BPgI+Ao8CjwLHAscC8gLyAhgDGANBA0EDagNqA4sDiwOhA6EDrQOtA7ADsAOuA64DrQOtA6gDqAOfA58DlgOWA48DjwOPA48DnwOfA7oDugPeA94DBAQEBCEEIQQxBDEENAQ0BCYEJgQOBA4E8APwA84DzgOoA6gDfgN+A0oDSgMNAw0DxwLHAnwCfAIwAjAC5wHnAaEBoQFdAV0BHAEcAdsA2wCcAJwAXwBfACIAIgDj/+P/nf+d/1D/UP/7/vv+n/6f/kL+Qv7k/eT9h/2H/S/9L/3d/N38j/yP/EX8Rfz4+/j7n/uf+zv7O/vF+sX6QPpA+rb5tvkv+S/5r/iv+EP4Q/jt9+33rveu94z3jPeF94X3lPeU97r3uvfy9/L3Mvgy+Hz4fPjI+Mj4DvkO+VD5UPmL+Yv5vPm8+en56fkI+gj6BvoG+tr52vlp+Wn5o/ij+JH3kfc49jj2qfSp9A/zD/OI8YjxMPAw8DbvNu+u7q7umu6a7gDvAO+9773voPCg8IbxhvFC8kLysvKy8tLy0vKZ8pnyCvIK8kTxRPFj8GPwie+J7+vu6+6j7qPuru6u7grvCu+T75PvI/Aj8LLwsvA88TzxxvHG8WfyZ/Iq8yrzEPQQ9B71HvVR9lH2mvea9+747vgw+jD6O/s7+/T79PtD/EP8Jfwl/Lb7tvse+x77ivqK+i76Lvov+i/6oPqg+oD7gPu+/L78M/4z/rH/sf8PAQ8BKwIrAvEC8QJlA2UDmgOaA6kDqQO1A7UD4APgAzgEOATGBMYEgAWABUcGRwYIBwgHtQe1B0IIQgi+CL4IPwk/Cc0JzQmACoAKawtrC4oMigznDecNeQ95DxIREhGGEoYSqBOoE0EUQRRJFEkU1xPXEwITAhP5EfkR7RDtEPMP8w8nDycPpA6kDmgOaA51DnUOwA7ADh0PHQ90D3QPtA+0D80PzQ/ND80PzA/MD8kPyQ/PD88P3w/fD+YP5g/cD9wPuw+7D2oPag/hDuEOIg4iDigNKA0IDAgM5grmCtgJ2An/CP8IcwhzCDQINAhBCEEIigiKCOwI7AhGCUYJfQl9CXgJeAk2CTYJwwjDCCgIKAhzB3MHsQaxBuYF5gUaBRoFWQRZBKkDqQMHAwcDbwJvAtUB1QE2ATYBmgCaABEAEQCv/6//g/+D/5D/kP/Q/9D/NwA3ALcAtwA9AT0BuQG5ARUCFQJAAkACMQIxAukB6QF0AXQB4wDjAEYARgCq/6r/GP8Y/5v+m/48/jz+//3//ef95/3v/e/9Dv4O/jr+Ov5t/m3+nv6e/sz+zP7w/vD+Bv8G/w3/Df8D/wP/6P7o/r3+vf59/n3+KP4o/sH9wf1Q/VD94vzi/If8h/xF/EX8HPwc/Av8C/wT/BP8Nvw2/H78fvzw/PD8h/2H/TX+Nf7r/uv+mf+Z/zMAMwC2ALYAHgEeAWgBaAGQAZABkwGTAXUBdQE/AT8B/QD9ALwAvACHAIcAZQBlAFsAWwBrAGsAkgCSAMkAyQAGAQYBQQFBAXMBcwGdAZ0BwgHCAeAB4AH0AfQB/AH8AfQB9AHYAdgBqwGrAWwBbAEYARgBsgCyADwAPADB/8H/Tf9N//D+8P6v/q/+jv6O/ov+i/6j/qP+0f7R/g//D/9P/0//gf+B/5b/lv+G/4b/VP9U/wz/DP+7/rv+aP5o/hf+F/7C/cL9af1p/RP9E/3C/ML8ePx4/DX8Nfzz+/P7rPus+2j7aPsu+y77CPsI+/36/foL+wv7KPso+077Tvt3+3f7mfuZ+7T7tPu7+7v7o/uj+3H7cfsr+yv74frh+qv6q/qW+pb6qPqo+uD64Po5+zn7rPus+zb8NvzP/M/8av1q/fz9/P18/nz+6P7o/kL/Qv+R/5H/2v/a/xwAHABZAFkAjgCOALoAugDbANsA7wDvAPIA8gDqAOoA4QDhAOcA5wAPAQ8BXwFfAdEB0QFYAlgC4wLjAl0DXQO6A7oD8QPxA/oD+gPWA9YDjwOPAzQDNAPXAtcCiQKJAk0CTQImAiYCEwITAhMCEwIgAiACLwIvAiwCLAIDAgMCsQGxAUEBQQHOAM4AdAB0ADwAPAAdAB0ACQAJAPf/9//s/+z/8P/w/wMAAwANAA0A8v/y/6L/ov8p/yn/rP6s/lT+VP42/jb+Sv5K/nr+ev64/rj+C/8L/4H/gf8VABUAqgCqAAwBDAEWARYByQDJAE0ATQDV/9X/fP98/zz/PP/6/vr+qP6o/lf+V/4n/if+Lf4t/lj+WP53/nf+W/5b/v39/f15/Xn9+/z7/J78nvxR/FH86vvq+1H7UfuF+oX6nfmd+bv4u/jW99b30PbQ9p31nfU99D300/LT8pzxnPGt8K3w9e/172bvZu/q7urug+6D7lruWu547njuwO7A7hLvEu897z3vM+8z7xvvG+8c7xzvQe9B74vvi+/U79Tv++/77xPwE/A08DTwcvBy8Nvw2/BY8VjxxvHG8SXyJfJ88nzy2PLY8k3zTfPU89TzWPRY9Nv02/Rf9V/16/Xr9Yn2ifYr9yv3u/e79zT4NPif+J/4DvkO+ZP5k/ks+iz6xvrG+lP7U/vR+9H7SvxK/ND80Pxk/WT9+/37/Yb+hv4D/wP/gP+A/w8ADwC9AL0AgQGBAUgCSAIGAwYDwwPDA4YEhgRcBVwFPwY/BhEHEQfAB8AHTwhPCMcIxwhHCUcJ4AngCYAKgAoZCxkLnwufCwsMCwxzDHMM7AzsDGsNaw3jDeMNSA5IDoQOhA6jDqMOuA64DsEOwQ6/Dr8Osg6yDo0OjQ5eDl4OPQ49DicOJw4iDiIOKg4qDisOKw4pDikOMw4zDkYORg5gDmAOfg5+DocOhw56DnoOZQ5lDkgOSA4qDioODA4MDtcN1w2MDYwNNw03Dd4M3gyODI4MTwxPDA0MDQzDC8MLeAt4CywLLAvrCusKuwq7CooKigpKCkoK9Qn1CYgJiAkOCQ4JkwiTCBMIEwiIB4gH8QbxBk0GTQaoBagFEAUQBYoEigQVBBUEsAOwA1cDVwMPAw8D3wLfAsMCwwKyArICogKiAogCiAJhAmECNAI0AgECAQLHAccBggGCAS4BLgHOAM4AbwBvABwAHADc/9z/rP+s/4f/h/9r/2v/Wf9Z/1T/VP9d/13/bP9s/3n/ef9+/37/eP94/23/bf9g/2D/Tv9O/zP/M/8L/wv/2f7Z/qf+p/5+/n7+YP5g/kv+S/44/jj+KP4o/iP+I/42/jb+aP5o/rb+tv4U/xT/c/9z/8r/yv8bABsAawBrALYAtgD2APYAHAEcASABIAEBAQEBzADMAI8AjwBRAFEAFQAVANn/2f+c/5z/Y/9j/zb/Nv8c/xz/FP8U/xr/Gv8o/yj/PP88/1f/V/95/3n/of+h/8j/yP/n/+f//f/9/wwADAAYABgAJwAnADYANgBBAEEARgBGAEUARQBDAEMARgBGAFIAUgBoAGgAhwCHAK4ArgDfAN8AGAEYAVcBVwGWAZYB0gHSAQICAgIjAiMCMQIxAigCKAIBAgECvQG9AWABYAHxAPEAfAB8AAcABwCR/5H/FP8U/5D+kP4G/gb+ev16/fj8+PyD/IP8GPwY/LX7tftX+1f7/Pr8+q/6r/p0+nT6R/pH+ij6KPoM+gz66vnq+cX5xfmg+aD5ffl9+WP5Y/lW+Vb5VvlW+Wz5bPmb+Zv53/nf+Tn6Ofqh+qH6D/sP+4L7gvv3+/f7avxq/Nn82fw+/T79k/2T/dn92f0V/hX+S/5L/oH+gf63/rf+6P7o/hP/E/83/zf/Vv9W/3T/dP+W/5b/u/+7/+T/5P8RABEAQgBCAHQAdACkAKQAzADMAOQA5ADtAO0A6QDpANwA3ADKAMoAtwC3AKIAogCOAI4AfgB+AHgAeAB/AH8AkACQAKYApgC5ALkAwgDCAL4AvgCvAK8AlwCXAHcAdwBQAFAAIwAjAPP/8//C/8L/kP+Q/13/Xf8l/yX/5/7n/qj+qP5t/m3+PP48/hz+HP4M/gz+CP4I/hD+EP4j/iP+QP5A/mn+af6b/pv+zv7O/v7+/v4l/yX/Qv9C/1r/Wv9w/3D/i/+L/63/rf/b/9v/FAAUAFcAVwCfAJ8A6ADoACsBKwFjAWMBjgGOAacBpwGoAagBjgGOAVEBUQHvAO8AZQBlALj/uP/o/uj++P34/fD88PzT+9P7p/qn+n75fvld+F34R/dH90z2TPZs9Wz1pPSk9AP0A/SD84PzGPMY88DywPJo8mjy/vH+8Yvxi/EO8Q7xivCK8BTwFPCr76vvSu9K7/vu++677rvuhu6G7nLucu6D7oPus+6z7gzvDO99733v8u/y727wbvDr8OvwZfFl8ezx7PF98n3yC/ML85DzkPP/8//zTfRN9Ij0iPS89Lz07vTu9C71LvV39Xf1wPXA9RD2EPZo9mj2zfbN9kn3Sffe9973g/iD+DX5Nfnw+fD5svqy+n/7f/tc/Fz8Rv1G/Tv+O/47/zv/RgBGAFgBWAFyAnICjwOPA6IEogSmBaYFnwafBocHhwdoCGgISQlJCR8KHwrtCu0Ktwu3C3QMdAwtDS0N6g3qDZwOnA5DD0MP4Q/hD2MQYxDNEM0QJhEmEV8RXxF9EX0RiRGJEXkReRFbEVsROhE6EQ0RDRHWENYQmRCZEE4QThD+D/4PvA+8D4APgA9ND00PJA8kD+8O7w6vDq8OcA5wDi4OLg7xDfENvw2/DYcNhw1FDUUNAA0ADbIMsgxpDGkMMQwxDAEMAQzaC9oLugu6C5ULlQtrC2sLQQtBCwwLDAvICsgKdwp3Cg0KDQqLCYsJ+gj6CFYIVgijB6MH6QbpBigGKAZoBWgFswSzBA0EDQR2A3YD7QLtAnACcAL7AfsBkQGRATQBNAHlAOUAoACgAGIAYgAjACMA3//f/5b/lv9J/0n/9/73/qb+pv5T/lP+A/4D/rv9u/2B/YH9W/1b/U79Tv1a/Vr9fv1+/bf9t/0C/gL+WP5Y/rT+tP4M/wz/V/9X/4z/jP+n/6f/p/+n/5P/k/9v/2//Qf9B/w//D//d/t3+r/6v/oz+jP59/n3+hf6F/qf+p/7h/uH+Lv8u/4b/hv/m/+b/SQBJAK0ArQAOAQ4BbAFsAb8BvwEFAgUCOgI6AlkCWQJmAmYCYwJjAlYCVgJHAkcCOwI7AjQCNAI0AjQCPgI+Ak8CTwJqAmoCkQKRAr4CvgLtAu0CGAMYAzUDNQNDA0MDQQNBAzADMAMRAxED6ALoArECsQJsAmwCHAIcAsQBxAFoAWgBDgEOAbcAtwBlAGUAHAAcAN3/3f+r/6v/hf+F/2z/bP9d/13/VP9U/1D/UP9R/1H/U/9T/1f/V/9Z/1n/V/9X/1L/Uv9L/0v/RP9E/0H/Qf9C/0L/R/9H/0//T/9Z/1n/Yf9h/2j/aP9q/2r/Zf9l/1f/V/89/z3/Ff8V/97+3v6Z/pn+RP5E/ub95v2C/YL9Hf0d/b78vvxl/GX8EPwQ/ML7wvt2+3b7Lvsu+/D68Pq++r76lvqW+nn6efph+mH6SfpJ+jb6Nvop+in6JPok+i36LfpA+kD6U/pT+mb6Zvp1+nX6fvp++or6ivqc+pz6s/qz+tP60/r6+vr6Kfsp+2T7ZPut+637A/wD/GL8YvzG/Mb8Jv0m/YD9gP3Q/dD9F/4X/lH+Uf5//n/+oP6g/rj+uP7N/s3+5P7k/gD/AP8f/x//PP88/1L/Uv9f/1//aP9o/2//b/94/3j/gv+C/4r/iv+N/43/jf+N/43/jf+S/5L/m/+b/6b/pv+t/63/rf+t/6f/p/+h/6H/n/+f/6b/pv+2/7b/zv/O/+z/7P8NAA0ALwAvAE4ATgBoAGgAdwB3AHkAeQBrAGsATwBPACUAJQDy//L/u/+7/4n/if9h/2H/RP9E/zP/M/8q/yr/Iv8i/x3/Hf8c/xz/Iv8i/zL/Mv9L/0v/af9p/4n/if+o/6j/xf/F/+L/4v/5//n/BwAHAAIAAgDl/+X/qv+q/1b/Vv/o/uj+Zv5m/tb91v08/Tz9nPyc/AH8Afxu+2774/rj+mX6Zfrt+e35d/l3+Qv5C/mq+Kr4U/hT+Az4DPjN9833i/eL90f3R/f59vn2n/af9kX2Rfbr9ev1jfWN9TX1NfXa9Nr0dfR19BP0E/S187XzYPNg8yjzKPMN8w3zBvMG8xbzFvMy8zLzTvNO83fzd/Ou867z8fPx80b0RvSj9KP09/T39EP1Q/WB9YH1tPW09ej16PUh9iH2WvZa9pf2l/bT9tP2CvcK90b3RveL94v31/fX9zL4MviZ+Jn4CPkI+YH5gfkE+gT6jPqM+hz7HPu0+7T7VPxU/P38/fyv/a/9Zv5m/h7/Hv/V/9X/iQCJADkBOQHpAekBmwKbAk0DTQMEBAQExgTGBJAFkAVpBmkGUgdSBz8IPwgrCSsJFwoXCvUK9QrJC8kLlwyXDFANUA3wDfANeQ55DuAO4A4vDy8Pdw93D7IPsg/lD+UPFhAWEDUQNRBIEEgQWhBaEGMQYxBuEG4QfxB/EIYQhhCEEIQQfxB/EGcQZxBBEEEQEhASEMwPzA9xD3EPCg8KD5AOkA4MDgwOhw2HDfoM+gxsDGwM5wvnC2kLaQv5CvkKoAqgClEKUQoLCgsKzAnMCYoJiglFCUUJ/gj+CKwIrAhMCEwI4AfgB2UHZQfiBuIGYgZiBuMF4wVpBWkF9gT2BIYEhgQbBBsEuQO5A14DXgMHAwcDsAKwAlMCUwLvAe8BhQGFARoBGgGtAK0APwA/AM3/zf9V/1X/1v7W/lb+Vv7V/dX9Vv1W/eH84fx4/Hj8Hfwd/Nb71vul+6X7hvuG+3v7e/uC+4L7lfuV+7T7tPvY+9j79/v3+xD8EPwd/B38H/wf/B38Hfwd/B38Ifwh/C38LfxB/EH8Xvxe/Ib8hvy+/L78BP0E/Vj9WP25/bn9Iv4i/pL+kv4K/wr/iP+I/wkACQCMAIwADgEOAYkBiQH9Af0BaAJoAsQCxAIOAw4DRQNFA2oDagODA4MDmwObA7MDswPRA9ED9AP0AxkEGQRABEAEbQRtBJkEmQTGBMYE7gTuBAkFCQUVBRUFFQUVBQoFCgX5BPkE5wTnBNIE0gS7BLsEpASkBIsEiwRxBHEEVwRXBDoEOgQYBBgE8wPzA80DzQOnA6cDhwOHA2kDaQNKA0oDLAMsAwoDCgPkAuQCvAK8AowCjAJOAk4CAQIBAqQBpAE6AToBzQDNAGEAYQD7//v/mf+Z/z//P//r/uv+of6h/mP+Y/4u/i7+/P38/cz9zP2Z/Zn9YP1g/Sf9J/3v/O/8tfy1/H78fvxH/Ef8EPwQ/OD74Pu2+7b7kPuQ+2/7b/tO+077K/sr+wr7Cvvt+u361vrW+sj6yPrB+sH6uvq6+rj6uPq5+rn6vvq++sr6yvrc+tz67frt+v/6//oO+w77GfsZ+yb7Jvs1+zX7RftF+1r7Wvty+3L7i/uL+6j7qPvH+8f75Pvk+wL8Avwe/B78NPw0/EX8RfxR/FH8VfxV/FP8U/xN/E38Q/xD/Dz8PPw4/Dj8N/w3/Dr8Ovw//D/8RfxF/E/8T/xf/F/8c/xz/I38jfyq/Kr8xPzE/Nv82/zv/O/8/vz+/Ar9Cv0S/RL9E/0T/Q79Dv0C/QL97/zv/N383fzR/NH8zPzM/NX81fzp/On8Bf0F/Sf9J/1O/U79eP14/aX9pf3U/dT9A/4D/i/+L/5U/lT+c/5z/o/+j/6s/qz+0P7Q/vv++/4t/y3/Yv9i/5b/lv/J/8n/+//7/y0ALQBeAF4AkACQAMAAwADtAO0AGAEYAUABQAFjAWMBfgF+AY4BjgGNAY0BdwF3AUgBSAH8APwAkwCTAA0ADQBt/23/uP64/vn9+f01/TX9c/xz/Lz7vPsP+w/7bfpt+tz53Pla+Vr54vji+H74fvgp+Cn43/ff96j3qPd99333VvdW9zj3OPcd9x33APcA9+v26/bb9tv2y/bL9sH2wfa29rb2pfal9pv2m/ac9pz2rPas9tf21/YZ9xn3Zvdm97v3u/cQ+BD4Yfhh+LT4tPgJ+Qn5Wvla+aX5pfnj+eP5DfoN+iv6K/o/+j/6TvpO+mD6YPpx+nH6ffp9+oX6hfqI+oj6iPqI+o36jfqb+pv6svqy+tb61voJ+wn7SPtI+5b7lvvz+/P7Xfxd/NP80/xT/VP92v3a/WT+ZP7x/vH+fv9+/wwADACbAJsALQEtAb8BvwFVAlUC7ALsAoEDgQMXBBcEsQSxBEoFSgXnBecFigaKBisHKwfNB80HdAh0CBcJFwm4CbgJWQpZCukK6QpmC2YLzwvPCxoMGgxODE4McgxyDIEMgQyCDIIMfgx+DHAMcAxiDGIMYQxhDGcMZwx5DHkMmwybDL8MvwzkDOQMDQ0NDS0NLQ1HDUcNXw1fDWkNaQ1pDWkNZA1kDVANUA0yDTINEQ0RDeMM4wysDKwMdAx0DDMMMwzvC+8LrguuC2gLaAshCyEL3greCpgKmApQClAKCAoICrUJtQlUCVQJ5gjmCGUIZQjRB9EHLgcuB3oGega4BbgF7QTtBB4EHgRQA1ADjAKMAtQB1AEqASoBjwCPAAYABgCN/43/Jv8m/9D+0P6J/on+TP5M/hn+Gf7r/ev9wv3C/Z79nv17/Xv9V/1X/TD9MP0I/Qj94Pzg/L/8v/yn/Kf8mPyY/JT8lPya/Jr8qvyq/Mf8x/zw/PD8Iv0i/Vf9V/2K/Yr9s/2z/dT91P3s/ez9+v36/QH+Af7//f/99P30/eH94f3K/cr9sf2x/Zv9m/2I/Yj9eP14/W/9b/1s/Wz9cf1x/YD9gP2X/Zf9tf21/dr92v0G/gb+Of45/nP+c/6x/rH+7v7u/if/J/9d/13/kP+Q/8P/w//8//z/OgA6AH4AfgDHAMcAFwEXAW8BbwHRAdEBPQI9AqwCrAIcAxwDiQOJA+4D7gNLBEsEogSiBO4E7gQvBS8FZwVnBZAFkAWvBa8FxwXHBdYF1gXeBd4F4gXiBd0F3QXTBdMFywXLBcIFwgW7BbsFuQW5BbQFtAWqBaoFmwWbBYMFgwVfBV8FMgUyBfYE9gSqBKoEUARQBOgD6AN0A3QD/AL8AoMCgwIOAg4CnwGfATcBNwHUANQAdgB2ABsAGwDD/8P/a/9r/xX/Ff+9/r3+YP5g/gD+AP6b/Zv9Mf0x/cj8yPxg/GD8+/v7+537nftG+0b79Pr0+qz6rPpu+m76Ovo6+hb6FvoE+gT6APoA+g36Dfon+if6R/pH+m36bfqU+pT6uPq4+tf61/rv+u/6+Pr4+vb69vrn+uf6zfrN+rD6sPqU+pT6efp5+mb6Zvpc+lz6V/pX+l36Xfpt+m36g/qD+qP6o/rJ+sn67/rv+hn7GftC+0L7Zftl+4T7hPub+5v7pful+6j7qPuh+6H7kPuQ+3v7e/ti+2L7Q/tD+yX7JfsM+wz7+Pr4+vP68/r8+vz6EPsQ+zH7Mfta+1r7hvuG+7X7tfvi++L7CvwK/C/8L/xO/E78Z/xn/Hv8e/yK/Ir8kvyS/Jf8l/ya/Jr8nPyc/KL8ovyt/K38ufy5/Mf8x/zX/Nf86vzq/AP9A/0n/Sf9VP1U/Yr9iv3G/cb9Av4C/j3+Pf5z/nP+ov6i/sr+yv7r/uv+Bf8F/xv/G/8v/y//RP9E/1z/XP95/3n/nP+c/8f/x//5//n/MAAwAGYAZgCVAJUAtQC1AMIAwgC4ALgAmACYAGQAZAAZABkAuf+5/0f/R//E/sT+Nf41/qT9pP0R/RH9f/x//PP78/tt+2377/rv+oP6g/op+in63/nf+an5qfmB+YH5Yflh+Uz5TPlA+UD5Nvk2+S/5L/kk+ST5D/kP+fT49PjV+NX4tfi1+KD4oPiZ+Jn4ofih+L74vvjw+PD4Mvky+Yj5iPnv+e/5Yvpi+uL64vpq+2r78vvy+3X8dfzu/O78WP1Y/bT9tP0D/gP+RP5E/nj+eP6c/pz+r/6v/rL+sv6r/qv+of6h/pr+mv6b/pv+p/6n/r3+vf7e/t7+Cv8K/0L/Qv+F/4X/0P/Q/x8AHwBtAG0AtwC3APsA+wA6AToBdQF1Aa0BrQHjAeMBGgIaAlICUgKNAo0CzwLPAhUDFQNhA2EDuAO4AxYEFgR/BH8E8wTzBG8FbwXzBfMFggaCBhMHEwekB6QHNQg1CLYItggoCSgJiwmLCdUJ1QkLCgsKMQoxCkMKQwpECkQKPwo/CjEKMQokCiQKIQohCh8KHwohCiEKKgoqCjAKMAo2CjYKRApECk4KTgpVClUKWQpZClAKUAo8CjwKJAokCgIKAgrbCdsJtAm0CYUJhQlOCU4JFQkVCdYI1giUCJQIVghWCBYIFgjYB9gHoQehB2sHawc2BzYHBQcFB9AG0AaVBpUGWAZYBhUGFQbOBc4FgwWDBS8FLwXRBNEEawRrBPwD/AOLA4sDHQMdA7MCswJMAkwC6QHpAYkBiQEsASwB1QDVAIQAhAA8ADwA/P/8/8X/xf+U/5T/aP9o/z7/Pv8U/xT/6f7p/r7+vv6X/pf+dP50/lb+Vv47/jv+I/4j/g7+Dv7//f/9+f35/QH+Af4U/hT+Lf4t/kr+Sv5m/mb+gP6A/pn+mf6w/rD+wv7C/s7+zv7S/tL+zv7O/sT+xP65/rn+rv6u/qb+pv6g/qD+nf6d/qD+oP6o/qj+uP64/s7+zv7o/uj+A/8D/yD/IP8+/z7/Xv9e/3//f/+f/5//vf+9/9j/2P/v/+//AwADABQAFAAiACIALQAtADcANwBCAEIAUABQAGMAYwB6AHoAkwCTAKwArADHAMcA5wDnAAsBCwEzATMBXQFdAYMBgwGlAaUBxgHGAeUB5QEGAgYCKgIqAk0CTQJuAm4CjgKOAq4CrgLQAtAC+AL4AiIDIgNNA00DdgN2A5kDmQO2A7YD0QPRA+UD5QPxA/ED9wP3A/ED8QPiA+IDzQPNA7IDsgOVA5UDdgN2A1IDUgMpAykD/AL8AskCyQKTApMCXAJcAiICIgLlAeUBoQGhAVQBVAH+AP4AnwCfADwAPADV/9X/bf9t/wT/BP+X/pf+J/4n/rf9t/1J/Un94Pzg/IX8hfw1/DX87/vv+7b7tvuF+4X7XPtc+z77Pvsq+yr7HPsc+xX7FfsQ+xD7B/sH+/36/frx+vH64Prg+tH60frD+sP6tvq2+qz6rPqj+qP6mPqY+pL6kvqO+o76jvqO+pT6lPqf+p/6qfqp+rX6tfrA+sD6xvrG+s36zfrQ+tD6zPrM+sL6wvqw+rD6lfqV+nb6dvpV+lX6M/oz+hX6Ffr7+fv54vni+dH50fnI+cj5xPnE+cz5zPnb+dv57fnt+Qb6Bvoh+iH6Pfo9+lz6XPp9+n36m/qb+rn6ufrV+tX67Prs+gP7A/sZ+xn7Lvsu+0X7Rftc+1z7cftx+4n7ifum+6b7xvvG+/D78Psi/CL8Wvxa/Jn8mfzc/Nz8Iv0i/Wn9af2w/bD98/3z/TL+Mv5r/mv+nP6c/sb+xv7q/ur+Cv8K/yb/Jv9B/0H/Xf9d/3v/e/+d/53/wf/B/+j/6P8SABIAPQA9AGkAaQCWAJYAwgDCAOgA6AAGAQYBGQEZAR0BHQETARMB+gD6ANIA0gCZAJkAUQBRAPv/+/+Z/5n/MP8w/8b+xv5b/lv+8/3z/ZP9k/07/Tv97Pzs/Kr8qvxy/HL8QPxA/BX8Ffzt++37xPvE+5/7n/t9+337WPtY+zb7NvsV+xX78/rz+tj62PrF+sX6uPq4+rb6tvq9+r36zPrM+ub65voO+w77QPtA+377fvvG+8b7E/wT/Gb8Zvy+/L78F/0X/XD9cP3D/cP9C/4L/kj+SP56/nr+pP6k/sr+yv7t/u3+C/8L/yT/JP85/zn/Tf9N/2T/ZP+D/4P/qf+p/9X/1f8FAAUAOQA5AG8AbwCoAKgA5ADkACIBIgFgAWABnwGfAdkB2QEQAhACRAJEAnMCcwKhAqEC0gLSAgYDBgNCA0IDiwOLA9oD2gMzBDMElASUBPkE+QRjBWMF0wXTBUEGQQarBqsGDgcOB2IHYgenB6cH4QfhBwwIDAgtCC0ISAhICFgIWAhhCGEIaAhoCGcIZwhkCGQIYwhjCF4IXghYCFgIVwhXCFQIVAhSCFIIVAhUCFEIUQhKCEoIQghCCDEIMQgZCBkI/gf+B9oH2gevB68HgweDB08HTwcYBxgH5AbkBrAGsAaCBoIGXwZfBkQGRAYvBi8GIwYjBhgGGAYNBg0GBgYGBvwF/AXuBe4F3QXdBb8FvwWUBZQFXQVdBRkFGQXLBMsEdgR2BBsEGwS7A7sDWwNbA/sC+wKfAp8CSAJIAvgB+AGvAa8BbwFvATgBOAEIAQgB3wDfALgAuACQAJAAZQBlADcANwAHAAcA1v/W/6P/o/9u/27/N/83/wH/Af/P/s/+pv6m/ov+i/59/n3+e/57/oX+hf6Y/pj+tP60/tn+2f4F/wX/Nv82/2j/aP+a/5r/yP/I//T/9P8cABwAPwA/AFsAWwBtAG0AdgB2AHkAeQB3AHcAdAB0AHAAcABsAGwAaQBpAGkAaQBtAG0AeAB4AIoAigCiAKIAvQC9ANgA2ADwAPAAAwEDARABEAEVARUBEwETAQsBCwH7APsA5QDlAMoAygCrAKsAjACMAG0AbQBTAFMAPgA+AC8ALwAmACYAIwAjACMAIwApACkAMwAzAEIAQgBUAFQAZgBmAHYAdgCCAIIAiQCJAIsAiwCJAIkAhQCFAIEAgQB+AH4AfwB/AIQAhACOAI4AngCeALMAswDNAM0A6wDrAAsBCwEqASoBSAFIAWABYAFzAXMBgQGBAYoBigGPAY8BjwGPAYoBigGBAYEBdAF0AWUBZQFTAVMBPgE+ASYBJgEKAQoB7ADsAMwAzACrAKsAiQCJAGQAZAA7ADsADQANANz/3P+o/6j/cv9y/zr/Ov/+/v7+vf69/nn+ef40/jT+7/3v/a/9r/10/XT9Pv0+/Q39Df3i/OL8ufy5/Jb8lvx5/Hn8Xfxd/Ef8R/wz/DP8H/wf/Az8DPz5+/n74/vj+8/7z/u9+737qvuq+5z7nPuO+477fft9+2z7bPta+1r7RvtG+zX7Nfsl+yX7FfsV+wf7B/v4+vj65vrm+tj62PrN+s36wPrA+rf6t/qt+q36n/qf+pL6kvqE+oT6dPp0+mb6ZvpY+lj6SPpI+jz6PPoz+jP6Lvou+jH6Mfo7+jv6RvpG+lX6Vfpm+mb6dvp2+on6ifqf+p/6tfq1+s/6z/rr+uv6BvsG+yT7JPtE+0T7ZPtk+4j7iPuu+6770/vT+/r7+vsf/B/8QvxC/GT8ZPyH/If8qPyo/Mv8y/zu/O78Dv0O/Sz9LP1G/Ub9Xf1d/XP9c/2K/Yr9oP2g/bj9uP3R/dH96/3r/Qn+Cf4p/in+TP5M/nL+cv6Z/pn+wf7B/un+6f4R/xH/O/87/2b/Zv+Q/5D/t/+3/9v/2//8//z/HAAcADwAPABcAFwAfAB8AJgAmACvAK8AwgDCANAA0ADeAN4A6wDrAPcA9wABAQEBCAEIAQwBDAEMAQwBCQEJAQIBAgH2APYA5ADkAMsAywCrAKsAhQCFAFoAWgArACsA+f/5/8X/xf+S/5L/Y/9j/zj/OP8S/xL/7/7v/s/+z/6x/rH+lf6V/nz+fP5m/mb+T/5P/jj+OP4g/iD+Bf4F/u397f3Y/dj9xv3G/bv9u/20/bT9sP2w/bH9sf22/bb9vf29/cj9yP3W/db95v3m/fv9+/0X/hf+OP44/l7+Xv6I/oj+t/63/un+6f4f/x//V/9X/47/jv/B/8H/7v/u/xUAFQA5ADkAXQBdAIMAgwCqAKoA0gDSAPoA+gAjASMBUAFQAYABgAGzAbMB6AHoARsCGwJKAkoCdwJ3AqMCowLOAs4C+QL5AiEDIQNFA0UDZgNmA38DfwOUA5QDpwOnA7QDtAO+A74DxwPHA84DzgPVA9UD3gPeA+gD6APxA/ED/gP+AwwEDAQcBBwEMAQwBEIEQgRQBFAEXgReBGcEZwRxBHEEfwR/BI8EjwSgBKAEswSzBMIEwgTRBNEE4gTiBPEE8QT/BP8EDgUOBRYFFgUcBRwFIgUiBSgFKAUvBS8FOgU6BUIFQgVJBUkFTwVPBVIFUgVUBVQFVgVWBVIFUgVJBUkFPQU9BSsFKwUXBRcFAwUDBe0E7QTWBNYEvwS/BKQEpASIBIgEbQRtBE4ETgQtBC0ECwQLBOUD5QO+A74DmAOYA3IDcgNLA0sDJgMmAwADAAPdAt0CvQK9Ap8CnwKDAoMCaAJoAksCSwIuAi4CEwITAvgB+AHgAeABygHKAbUBtQGiAaIBkwGTAYcBhwF/AX8BegF6AXYBdgFyAXIBbgFuAWkBaQFjAWMBXQFdAVYBVgFOAU4BRgFGAT0BPQE2ATYBMQExAS0BLQErASsBKQEpAScBJwEkASQBIgEiAR8BHwEcARwBGgEaARoBGgEZARkBGAEYARQBFAENAQ0BAwEDAfYA9gDoAOgA1wDXAMQAxACtAK0AlACUAHoAegBhAGEASwBLADcANwAlACUAEgASAP/////r/+v/2P/Y/8b/xv+2/7b/qP+o/53/nf+T/5P/jP+M/4n/if+H/4f/hv+G/4T/hP+C/4L/gP+A/3//f/9//3//gP+A/4P/g/+G/4b/iv+K/5L/kv+d/53/rP+s/7z/vP/M/8z/2//b/+f/5//w//D/+P/4//3//f8AAAAAAAAAAP3//f/4//j/8//z/+3/7f/o/+j/4v/i/9z/3P/V/9X/z//P/8j/yP/B/8H/uf+5/6//r/+k/6T/mP+Y/43/jf+B/4H/dP90/2X/Zf9T/1P/Pf89/yX/Jf8L/wv/7/7v/tP+0/61/rX+l/6X/nn+ef5e/l7+Rv5G/jL+Mv4h/iH+Ef4R/gL+Av7z/fP94/3j/dT91P3C/cL9qv2q/Y79jv1t/W39Rv1G/R79Hv31/PX8yvzK/KH8ofx5/Hn8T/xP/Cj8KPwD/AP83fvd+7r7uvuZ+5n7eft5+1/7X/tK+0r7Ofs5+y37Lfsk+yT7G/sb+xT7FPsO+w77BfsF+/36/frz+vP65frl+tn62frO+s76w/rD+r36vfq5+rn6tfq1+rT6tPq1+rX6tvq2+rz6vPrF+sX6z/rP+t763vrx+vH6BPsE+xz7HPs1+zX7S/tL+2L7Yvt3+3f7iPuI+5n7mfup+6n7t/u3+8b7xvvX+9f76Pvo+/37/fsU/BT8K/wr/ET8RPxe/F78efx5/JX8lfyy/LL8zPzM/Of85/wB/QH9Gv0a/Tb9Nv1T/VP9cP1w/Y/9j/2t/a39zP3M/e397f0Q/hD+M/4z/lb+Vv52/nb+lf6V/rP+s/7S/tL+8f7x/hD/EP8s/yz/Rf9F/1z/XP9z/3P/if+J/6D/oP+2/7b/yf/J/9r/2v/p/+n/+f/5/woACgAeAB4AMgAyAEUARQBVAFUAZQBlAHMAcwCBAIEAjgCOAJcAlwCdAJ0AngCeAJwAnACWAJYAjACMAH8AfwBvAG8AXQBdAE0ATQA/AD8ANgA2ADEAMQAtAC0AKgAqACYAJgAiACIAHwAfAB0AHQAbABsAGwAbABsAGwAcABwAHwAfACQAJAArACsANAA0AD4APgBLAEsAWgBaAGsAawB/AH8AlwCXALAAsADNAM0A7QDtABEBEQE6AToBZQFlAZEBkQG6AboB4gHiAQYCBgInAicCSAJIAmUCZQJ+An4ClAKUAqUCpQKxArECuwK7AsICwgLIAsgCzgLOAtEC0QLRAtEC0QLRAtEC0QLRAtEC1QLVAtcC1wLZAtkC2QLZAtUC1QLQAtACyQLJAr8CvwKyArICogKiAo8CjwJ7AnsCawJrAl8CXwJYAlgCVAJUAk8CTwJKAkoCSAJIAkcCRwJJAkkCUAJQAlYCVgJbAlsCXgJeAl0CXQJbAlsCWAJYAlICUgJJAkkCPgI+AjACMAIgAiACEAIQAv4B/gHrAesB1gHWAcABwAGrAasBmAGYAYYBhgF0AXQBYgFiAU4BTgE7ATsBLAEsASABIAEVARUBCwELAf8A/wDxAPEA5ADkANoA2gDVANUA1ADUANcA1wDdAN0A6ADoAPcA9wAMAQwBJQElAT4BPgFXAVcBbQFtAYIBggGYAZgBrwGvAcYBxgHbAdsB7QHtAf0B/QENAg0CIQIhAjYCNgJMAkwCYQJhAnMCcwKCAoICkQKRAp8CnwKvAq8CwQLBAtAC0ALdAt0C6QLpAvIC8gL4AvgC/AL8AvsC+wL1AvUC7ALsAt8C3wLQAtACwgLCArQCtAKnAqcCnwKfApkCmQKWApYClgKWApcClwKXApcCmAKYApcClwKYApgCmwKbAp4CngKhAqECpAKkAqMCowKfAp8CmAKYAowCjAJ7AnsCZwJnAlACUAI4AjgCIwIjAhACEAL/Af8B8QHxAeMB4wHWAdYBywHLAcIBwgG6AboBsQGxAaUBpQGUAZQBgAGAAWkBaQFQAVABNwE3ARwBHAH+AP4A3ADcALcAtwCSAJIAbQBtAEwATAAuAC4AEgASAPr/+v/i/+L/y//L/7T/tP+d/53/hv+G/3H/cf9e/17/TP9M/zz/PP8s/yz/Gv8a/wf/B//z/vP+4f7h/tP+0/7H/sf+vP68/rT+tP6v/q/+rf6t/rL+sv68/rz+yv7K/tn+2f7q/ur++f75/gr/Cv8b/xv/K/8r/zr/Ov9F/0X/TP9M/1D/UP9T/1P/Vf9V/1f/V/9Y/1j/Vv9W/1P/U/9R/1H/UP9Q/1D/UP9Q/1D/TP9M/0P/Q/83/zf/KP8o/xr/Gv8M/wz//P78/uj+6P7P/s/+sP6w/pD+kP5v/m/+T/5P/jD+MP4S/hL+9P30/dr92v3F/cX9sv2y/aT9pP2Y/Zj9iv2K/X79fv1y/XL9Zv1m/V39Xf1T/VP9SP1I/T/9P/02/Tb9L/0v/Sn9Kf0j/SP9GP0Y/Qr9Cv36/Pr86fzp/Nz83PzV/NX80fzR/NL80vzU/NT81PzU/NX81fzW/Nb81PzU/ND80PzI/Mj8uvy6/Kr8qvyZ/Jn8h/yH/Hj8ePxr/Gv8YPxg/Fj8WPxT/FP8T/xP/FD8UPxV/FX8W/xb/Gb8Zvx2/Hb8h/yH/J78nvy2/Lb8zPzM/N783vzs/Oz88vzy/Pb89vz4/Pj8+vz6/P/8//wH/Qf9Dv0O/Rf9F/0i/SL9LP0s/Tr9Ov1J/Un9Wv1a/Wz9bP2A/YD9lP2U/av9q/3E/cT93f3d/fj9+P0R/hH+Jv4m/jf+N/5C/kL+Sf5J/kz+TP5N/k3+UP5Q/lb+Vv5k/mT+d/53/pD+kP6s/qz+yP7I/uT+5P7//v/+Gv8a/zb/Nv9R/1H/av9q/4L/gv+W/5b/p/+n/7b/tv/A/8D/x//H/8j/yP/F/8X/v/+//7j/uP+y/7L/rf+t/6r/qv+q/6r/rP+s/7L/sv+7/7v/xf/F/83/zf/S/9L/1P/U/9P/0//R/9H/0P/Q/8//z//P/8//zf/N/8v/y//K/8r/y//L/87/zv/V/9X/3//f/+z/7P/6//r/DAAMAB8AHwA1ADUATQBNAGYAZgCAAIAAmwCbALQAtADKAMoA2wDbAOYA5gDsAOwA8ADwAPMA8wD3APcA/AD8AAMBAwEKAQoBFQEVASQBJAE2ATYBSwFLAWABYAFzAXMBhAGEAZIBkgGdAZ0BpwGnAa0BrQGxAbEBswGzAbMBswG1AbUBuAG4AbsBuwG8AbwBvQG9Ab4BvgHBAcEBywHLAdcB1wHlAeUB8wHzAf4B/gEGAgYCDgIOAhQCFAIYAhgCGgIaAhYCFgIMAgwC/wH/Ae8B7wHcAdwBxwHHAa4BrgGTAZMBeAF4AWABYAFLAUsBOQE5ASoBKgEcARwBEQERAQkBCQEEAQQBAQEBAfwA/ADxAPEA4ADgAMwAzAC2ALYAogCiAJEAkQCCAIIAdgB2AGwAbABmAGYAZgBmAGoAagBwAHAAeQB5AIMAgwCQAJAAnwCfALIAsgDGAMYA2wDbAO0A7QD+AP4ADwEPAR4BHgErASsBNAE0ATgBOAE2ATYBMwEzATABMAEwATABMwEzATgBOAE/AT8BRgFGAU0BTQFVAVUBXgFeAWcBZwFvAW8BeAF4AX8BfwGGAYYBjgGOAZMBkwGWAZYBlwGXAZUBlQGRAZEBjAGMAYcBhwGDAYMBgQGBAYMBgwGJAYkBlAGUAaIBogGyAbIBwwHDAdIB0gHfAd8B7AHsAfUB9QH7AfsBAAIAAgICAgIFAgUCCwILAhACEAIWAhYCGwIbAh8CHwIkAiQCLgIuAjsCOwJKAkoCWwJbAmgCaAJ0AnQCfwJ/AogCiAKPAo8ClAKUApECkQKIAogCfQJ9AnECcQJmAmYCXAJcAlICUgJFAkUCOAI4AisCKwIfAh8CFgIWAgoCCgL8AfwB6wHrAdkB2QHJAckBuwG7Aa0BrQGcAZwBhQGFAWgBaAFIAUgBJwEnAQcBBwHoAOgAyADIAKgAqACMAIwAdwB3AGsAawBlAGUAYgBiAGAAYABeAF4AXABcAF0AXQBgAGAAYwBjAGMAYwBfAF8AWABYAFIAUgBNAE0ASgBKAEQARAA7ADsALQAtAB8AHwATABMADQANAAsACwALAAsACgAKAAoACgAKAAoADgAOABQAFAAYABgAGAAYABEAEQAFAAUA9v/2/+T/5P/Q/9D/uP+4/53/nf+A/4D/Y/9j/0n/Sf80/zT/IP8g/w//D/8A/wD/8v7y/un+6f7j/uP+3/7f/tz+3P7Z/tn+1P7U/tD+0P7N/s3+yP7I/r/+v/6y/rL+nv6e/on+if5y/nL+Xf5d/kr+Sv45/jn+Kf4p/h7+Hv4W/hb+Ef4R/g/+D/4N/g3+CP4I/gL+Av75/fn97v3u/eT95P3Y/dj9yf3J/bn9uf2n/af9lf2V/YX9hf12/Xb9Zv1m/Vj9WP1L/Uv9QP1A/Tv9O/08/Tz9Qf1B/Ur9Sv1V/VX9X/1f/Wn9af1z/XP9ev16/YD9gP2E/YT9hf2F/Yf9h/2J/Yn9if2J/Yr9iv2L/Yv9i/2L/Y39jf2T/ZP9mv2a/aP9o/2u/a79uv26/cf9x/3X/df95f3l/fP98/38/fz9//3//f79/v35/fn98/3z/ez97P3k/eT92v3a/dL90v3M/cz9yv3K/c79zv3W/db93/3f/er96v33/ff9B/4H/hz+HP41/jX+Tv5O/mf+Z/5//n/+lf6V/qz+rP7C/sL+1f7V/ub+5v7y/vL++v76/gL/Av8M/wz/Gf8Z/yj/KP87/zv/T/9P/2b/Zv+A/4D/nP+c/7f/t//Q/9D/5P/k//T/9P8BAAEADAAMABYAFgAeAB4AIQAhACAAIAAaABoAEgASAAcABwD7//v/7f/t/9//3//R/9H/xf/F/77/vv+7/7v/u/+7/7z/vP+9/73/vf+9/7v/u/+3/7f/sf+x/6n/qf+e/57/kv+S/4b/hv96/3r/b/9v/2f/Z/9g/2D/W/9b/1j/WP9Y/1j/XP9c/2P/Y/9w/3D/gP+A/5L/kv+l/6X/t/+3/8f/x//W/9b/5P/k//H/8f/9//3/CAAIABAAEAAXABcAHAAcACAAIAAlACUAKQApACwALAAvAC8AMgAyADYANgA8ADwAQwBDAEkASQBPAE8AUwBTAFYAVgBXAFcAVwBXAFMAUwBLAEsAPwA/ADIAMgAlACUAGAAYAA0ADQACAAIA9//3/+v/6//h/+H/2v/a/9b/1v/V/9X/1f/V/9X/1f/W/9b/2f/Z/97/3v/l/+X/7P/s//H/8f/0//T/+P/4//7//v8GAAYAEAAQABkAGQAiACIAKgAqADUANQBDAEMAVQBVAGcAZwB4AHgAhwCHAJUAlQCjAKMAsgCyAMAAwADLAMsA0QDRANIA0gDSANIA0gDSANQA1ADWANYA2QDZANoA2gDaANoA3QDdAOIA4gDoAOgA7wDvAPUA9QD6APoA/wD/AAYBBgEPAQ8BGQEZASIBIgEoASgBLgEuATEBMQE1ATUBOQE5AT0BPQFBAUEBRgFGAU0BTQFXAVcBZwFnAXsBewGQAZABpwGnAbsBuwHPAc8B4wHjAfUB9QEGAgYCFQIVAiECIQIqAioCMwIzAjoCOgJAAkACRwJHAksCSwJOAk4CUQJRAlQCVAJYAlgCXwJfAmcCZwJwAnACegJ6AoMCgwKMAowClAKUApkCmQKcApwCngKeApwCnAKYApgClgKWApQClAKSApICkgKSApACkAKOAo4CiwKLAocChwKEAoQChAKEAoQChAKEAoQChAKEAoECgQJ+An4CewJ7AncCdwJwAnACZgJmAlcCVwJEAkQCMQIxAh0CHQILAgsC+wH7AekB6QHYAdgBxwHHAbYBtgGnAacBmAGYAYgBiAF2AXYBYwFjAVABUAE+AT4BLQEtARsBGwEHAQcB8QDxANcA1wC9AL0ApACkAIwAjAB2AHYAYQBhAE0ATQA7ADsALAAsACIAIgAbABsAFgAWABEAEQALAAsAAwADAPv/+//z//P/7P/s/+X/5f/e/97/1v/W/83/zf/C/8L/t/+3/6v/q/+f/5//k/+T/4n/if+B/4H/e/97/3f/d/90/3T/cf9x/23/bf9p/2n/ZP9k/13/Xf9U/1T/SP9I/zv/O/8s/yz/G/8b/wv/C//6/vr+6P7o/tj+2P7K/sr+vv6+/rb+tv6w/rD+rP6s/qv+q/6s/qz+sP6w/rX+tf67/rv+vv6+/r/+v/6+/r7+u/67/rj+uP62/rb+tP60/rL+sv6w/rD+rf6t/qz+rP6s/qz+rf6t/rD+sP6z/rP+tv62/rz+vP7E/sT+zf7N/tb+1v7e/t7+5P7k/uj+6P7r/uv+6v7q/uf+5/7f/t/+1P7U/sb+xv65/rn+rv6u/qX+pf6c/pz+kv6S/on+if6B/oH+ev56/nj+eP55/nn+ev56/nz+fP5+/n7+f/5//oL+gv6E/oT+g/6D/oD+gP56/nr+cv5y/mv+a/5m/mb+Yf5h/l3+Xf5Y/lj+U/5T/lD+UP5S/lL+V/5X/mD+YP5q/mr+cv5y/nv+e/6E/oT+kP6Q/p3+nf6q/qr+tP60/rv+u/7A/sD+w/7D/sf+x/7M/sz+z/7P/tL+0v7V/tX+2f7Z/uD+4P7p/un+8/7z/vz+/P4G/wb/EP8Q/xz/HP8r/yv/Ov86/0n/Sf9U/1T/XP9c/2P/Y/9o/2j/bP9s/3D/cP9z/3P/c/9z/3P/c/90/3T/d/93/3z/fP+D/4P/iv+K/5H/kf+Y/5j/nv+e/6L/ov+l/6X/pf+l/6L/ov+e/57/mv+a/5T/lP+P/4//iP+I/4D/gP93/3f/b/9v/2f/Z/9i/2L/Xv9e/1v/W/9Z/1n/WP9Y/1f/V/9X/1f/V/9X/1j/WP9Z/1n/Wf9Z/1n/Wf9W/1b/U/9T/07/Tv9J/0n/Q/9D/z7/Pv85/zn/M/8z/y7/Lv8q/yr/J/8n/yj/KP8s/yz/Mv8y/zr/Ov9C/0L/Sv9K/1L/Uv9Y/1j/Xv9e/2L/Yv9l/2X/Zv9m/2f/Z/9o/2j/af9p/2n/af9o/2j/Zv9m/2X/Zf9l/2X/Z/9n/2r/av9v/2//dP90/3n/ef9//3//h/+H/4//j/+Y/5j/oP+g/6f/p/+s/6z/sf+x/7b/tv+8/7z/wv/C/8j/yP/O/87/1P/U/9v/2//i/+L/6//r//X/9f8AAAAADAAMABoAGgAqACoAOwA7AEwATABcAFwAaQBpAHQAdAB+AH4AiACIAJEAkQCaAJoAoQChAKcApwCsAKwAsQCxALcAtwC9AL0AxADEAMsAywDRANEA2ADYAOAA4ADpAOkA8gDyAPsA+wACAQIBCAEIAQwBDAEQARABEgESARMBEwESARIBEAEQAQ8BDwEOAQ4BDwEPAREBEQESARIBFAEUARQBFAEVARUBFgEWARcBFwEZARkBHAEcAR4BHgEhASEBJQElASkBKQEtAS0BMgEyATcBNwE6AToBPQE9AUABQAFCAUIBRwFHAUsBSwFRAVEBVwFXAV0BXQFjAWMBagFqAXEBcQF4AXgBfwF/AYQBhAGIAYgBjQGNAZEBkQGVAZUBmgGaAZ0BnQGgAaABpAGkAagBqAGtAa0BtQG1AbwBvAHCAcIByAHIAc0BzQHSAdIB2QHZAeAB4AHlAeUB6AHoAegB6AHoAegB6AHoAegB6AHpAekB6QHpAecB5wHkAeQB4gHiAeAB4AHfAd8B3wHfAd0B3QHaAdoB2AHYAdcB1wHWAdYB1gHWAdQB1AHOAc4BxwHHAb4BvgG1AbUBrAGsAaIBogGUAZQBhAGEAXEBcQFfAV8BUQFRAUUBRQE6AToBLwEvASMBIwEXARcBDAEMAQMBAwH6APoA7wDvAOIA4gDUANQAxgDGALoAugCvAK8AowCjAJQAlACDAIMAcQBxAGAAYABSAFIARQBFADgAOAApACkAGgAaAAwADAABAAEA+v/6//T/9P/s/+z/4v/i/9b/1v/K/8r/vv++/7T/tP+p/6n/m/+b/43/jf9+/37/cf9x/2b/Zv9c/1z/Uv9S/0b/Rv85/zn/Lf8t/yL/Iv8a/xr/Ev8S/wr/Cv8B/wH/+P74/vH+8f7s/uz+6P7o/uT+5P7f/t/+2P7Y/tL+0v7N/s3+yv7K/sv+y/7M/sz+zf7N/s7+zv7Q/tD+0v7S/tb+1v7b/tv+4P7g/uT+5P7n/uf+6v7q/u7+7v7y/vL+9f71/vf+9/73/vf+9v72/vX+9f71/vX+9v72/vj++P74/vj++P74/vj++P75/vn++/77/v7+/v4A/wD/Af8B/wH/Af8C/wL/Av8C/wP/A/8G/wb/B/8H/wj/CP8I/wj/B/8H/wf/B/8I/wj/CP8I/wr/Cv8M/wz/Dv8O/xP/E/8a/xr/Iv8i/yv/K/80/zT/PP88/0P/Q/9K/0r/Uf9R/1j/WP9e/17/Y/9j/2f/Z/9r/2v/b/9v/3P/c/95/3n/fv9+/4L/gv+G/4b/iv+K/43/jf+S/5L/lv+W/5v/m/+g/6D/pP+k/6j/qP+s/6z/sP+w/7X/tf+5/7n/vP+8/73/vf++/77/vv++/77/vv+//7//v/+//7//v/+//7//wf/B/8P/w//H/8f/zP/M/9D/0P/S/9L/0//T/9P/0//T/9P/1P/U/9T/1P/T/9P/z//P/8n/yf/B/8H/uP+4/67/rv+k/6T/mf+Z/43/jf+A/4D/dP90/2j/aP9c/1z/UP9Q/0T/RP83/zf/Kv8q/x3/Hf8P/w////7//vD+8P7g/uD+0P7Q/sH+wf6z/rP+pv6m/pn+mf6M/oz+f/5//nT+dP5r/mv+Y/5j/l/+X/5c/lz+Wv5a/lr+Wv5c/lz+Xv5e/mP+Y/5o/mj+bv5u/nX+df58/nz+hP6E/o3+jf6Y/pj+o/6j/rD+sP6//r/+z/7P/uD+4P7x/vH+Af8B/xH/Ef8h/yH/Mv8y/0T/RP9V/1X/Zf9l/3T/dP+C/4L/j/+P/5v/m/+m/6b/sP+w/7n/uf/D/8P/zv/O/9v/2//q/+r/+P/4/wUABQAQABAAGgAaACMAIwAsACwANgA2AD8APwBGAEYATABMAFEAUQBYAFgAXwBfAGcAZwBuAG4AdAB0AHkAeQB+AH4AhACEAIoAigCSAJIAmQCZAJ8AnwCmAKYArgCuALcAtwDAAMAAyQDJANAA0ADWANYA3QDdAOUA5QDtAO0A9QD1APoA+gD/AP8AAwEDAQYBBgEKAQoBDwEPARIBEgEUARQBFQEVARYBFgEXARcBGQEZARoBGgEaARoBGAEYARQBFAERAREBDwEPAQwBDAEJAQkBBAEEAf4A/gD3APcA8ADwAOoA6gDkAOQA3QDdANYA1gDOAM4AxwDHAMIAwgC/AL8AvQC9ALsAuwC5ALkAuQC5ALgAuAC5ALkAuwC7ALwAvAC9AL0AvQC9AL4AvgC/AL8AwADAAMIAwgDCAMIAwQDBAMAAwAC+AL4AvQC9ALwAvAC7ALsAugC6ALgAuAC2ALYAtQC1ALMAswCxALEAsACwAK4ArgCsAKwAqgCqAKkAqQCpAKkAqQCpAKoAqgCtAK0AsQCxALcAtwC9AL0AwwDDAMkAyQDQANAA1wDXAN4A3gDmAOYA7wDvAPYA9gD+AP4ABgEGAQ4BDgEXARcBIAEgASgBKAExATEBOQE5AUIBQgFKAUoBUwFTAVkBWQFeAV4BYQFhAWIBYgFkAWQBZgFmAWYBZgFlAWUBYgFiAV0BXQFWAVYBTwFPAUYBRgE7ATsBLAEsARoBGgEHAQcB8wDzAOAA4ADNAM0AuQC5AKQApACNAI0AeAB4AGMAYwBSAFIAQgBCADMAMwAlACUAGAAYAA0ADQAEAAQA/v/+//n/+f/1//X/8P/w/+v/6//o/+j/5v/m/+X/5f/j/+P/4f/h/9//3//e/97/3f/d/93/3f/d/93/3f/d/9z/3P/a/9r/2f/Z/9j/2P/Y/9j/1v/W/9L/0v/M/8z/xf/F/73/vf+0/7T/rP+s/6P/o/+Z/5n/j/+P/4X/hf98/3z/dP90/23/bf9m/2b/X/9f/1n/Wf9T/1P/Tv9O/0n/Sf9E/0T/Qf9B/z7/Pv89/z3/Pf89/z//P/9C/0L/RP9E/0j/SP9M/0z/U/9T/1v/W/9m/2b/cv9y/37/fv+K/4r/lf+V/6H/of+u/67/uv+6/8f/x//T/9P/3//f/+r/6v/2//b/AgACAA0ADQAXABcAHgAeACMAIwAnACcAKgAqACoAKgApACkAJQAlAB8AHwAYABgAEAAQAAcABwD9//3/8f/x/+T/5P/V/9X/xv/G/7f/t/+o/6j/mf+Z/4n/if95/3n/Z/9n/1b/Vv9F/0X/Mv8y/x7/Hv8L/wv/+P74/uf+5/7X/tf+yf7J/rz+vP6w/rD+pf6l/p7+nv6Z/pn+mP6Y/pn+mf6a/pr+nP6c/p/+n/6k/qT+q/6r/rX+tf7B/sH+zP7M/tr+2v7o/uj++P74/gr/Cv8d/x3/L/8v/0L/Qv9V/1X/af9p/37/fv+T/5P/qP+o/7v/u//M/8z/3f/d/+7/7v8AAAAAEQARACEAIQAuAC4AOQA5AEEAQQBGAEYASABIAEYARgBBAEEAOQA5AC4ALgAiACIAFAAUAAUABQD2//b/5v/m/9f/1//I/8j/t/+3/6X/pf+R/5H/e/97/2P/Y/9L/0v/M/8z/xz/HP8G/wb/8f7x/t/+3/7R/tH+x/7H/sD+wP6+/r7+vf69/sH+wf7I/sj+0f7R/tz+3P7n/uf+8f7x/vv++/4F/wX/D/8P/xr/Gv8k/yT/Lf8t/zb/Nv8//z//Sf9J/1X/Vf9j/2P/cP9w/33/ff+I/4j/kv+S/5z/nP+m/6b/sP+w/7n/uf/C/8L/yf/J/9H/0f/Y/9j/3//f/+T/5P/o/+j/6v/q/+r/6v/p/+n/6P/o/+b/5v/j/+P/4f/h/97/3v/e/97/3v/e/+D/4P/j/+P/5//n/+z/7P/y//L/+P/4//7//v8EAAQACwALABIAEgAaABoAJQAlADEAMQA8ADwASABIAFUAVQBiAGIAcgByAIIAggCRAJEAnQCdAKUApQCrAKsAsQCxALUAtQC3ALcAuAC4ALcAtwC2ALYAtwC3ALoAugC/AL8AwwDDAMcAxwDHAMcAxwDHAMUAxQDCAMIAvQC9ALQAtACnAKcAmQCZAI0AjQCDAIMAfQB9AHoAegB4AHgAeAB4AHgAeAB6AHoAfQB9AH8AfwB/AH8AfQB9AHgAeAByAHIAbQBtAGgAaABiAGIAWgBaAFIAUgBMAEwASgBKAEsASwBOAE4AUgBSAFUAVQBYAFgAXABcAGAAYABjAGMAZQBlAGQAZABhAGEAXgBeAFwAXABbAFsAWgBaAFoAWgBbAFsAXgBeAGIAYgBmAGYAawBrAG4AbgBxAHEAdAB0AHkAeQB/AH8AhQCFAIoAigCMAIwAjACMAIwAjACOAI4AkACQAJEAkQCSAJIAkgCSAJMAkwCWAJYAnACcAKMAowCqAKoAsQCxALgAuADBAMEAzgDOANsA2wDnAOcA8gDyAPoA+gAAAQABCAEIAQ8BDwEUARQBFwEXARUBFQERAREBDgEOAQsBCwEJAQkBBgEGAf8A/wD2APYA6wDrAOAA4ADUANQAxwDHALgAuAClAKUAkgCSAIAAgABxAHEAYwBjAFcAVwBLAEsAPwA/ADQANAAtAC0AJwAnACMAIwAfAB8AGQAZABIAEgAMAAwABwAHAAIAAgD//////P/8//n/+f/3//f/9v/2//T/9P/w//D/6//r/+X/5f/f/9//2P/Y/9H/0f/J/8n/wf/B/7j/uP+x/7H/rP+s/6j/qP+l/6X/ov+i/5//n/+d/53/nP+c/5z/nP+d/53/nf+d/53/nf+f/5//o/+j/6v/q/+0/7T/v/+//8n/yf/U/9T/4f/h//D/8P8AAAAADwAPABkAGQAfAB8AIQAhACEAIQAgACAAHAAcABUAFQAMAAwAAQABAPf/9//v/+//6f/p/+H/4f/X/9f/yv/K/7z/vP+w/7D/pf+l/5n/mf+M/4z/fP98/27/bv9j/2P/Xf9d/13/Xf9f/1//Yv9i/2b/Zv9t/23/d/93/4b/hv+X/5f/p/+n/7X/tf/E/8T/1f/V/+j/6P/9//3/EgASACUAJQA3ADcATQBNAGYAZgCBAIEAmgCaAK4ArgC5ALkAvgC+AL8AvwC8ALwAtQC1AKcApwCRAJEAdgB2AFwAXABFAEUAMwAzACMAIwASABIA/////+v/6//Z/9n/yf/J/7f/t/+h/6H/hP+E/2P/Y/9E/0T/LP8s/xv/G/8Q/xD/B/8H/wD/AP/+/v7+BP8E/xT/FP8o/yj/Ov86/0T/RP9F/0X/P/8//zX/Nf8n/yf/FP8U//r++v7Z/tn+tv62/pf+l/5//n/+bv5u/l/+X/5N/k3+N/43/h7+Hv4D/gP+4v3i/bv9u/2I/Yj9Rv1G/f38/fy0/LT8cvxy/D/8P/wc/Bz8CvwK/A78Dvwt/C38Z/xn/Lz8vPwn/Sf9oP2g/SH+If6q/qr+OP84/8n/yf9cAFwA8ADwAIIBggEVAhUCrwKvAk8DTwP2A/YDowSjBEsFSwXxBfEFlQaVBi8HLwe8B7wHNwg3CJIIkgjLCMsI5gjmCOEI4QjCCMIIkAiQCEUIRQjpB+kHhQeFBx0HHQe4BrgGWAZYBvQF9AWHBYcFEQURBZAEkAQHBAcEeQN5A+IC4gI8AjwCiAGIAc4AzgAXABcAav9q/87+zv49/j3+s/2z/TP9M/3B/MH8YPxg/BX8FfzY+9j7nfud+2L7Yvsl+yX76frp+rn6ufqQ+pD6Z/pn+j76PvoT+hP67fnt+dn52fna+dr56fnp+QD6APoU+hT6IPog+i76Lvo++j76TfpN+ln6WfpX+lf6RfpF+i36LfoY+hj6C/oL+gv6C/oR+hH6F/oX+iL6Ivo3+jf6WfpZ+ov6i/rG+sb6/Pr8+i77Lvtc+1z7i/uL+8L7wvv/+//7Pfw9/Hn8efy3/Lf8AP0A/Vz9XP3Q/dD9Vf5V/uT+5P57/3v/GAAYALsAuwBjAWMBCAIIAp0CnQIbAxsDhAOEA9wD3AMtBC0EfAR8BMQExAQHBQcFSwVLBZIFkgXoBegFTwZPBrgGuAYYBxgHZwdnB5sHmwe3B7cHwQfBB7AHsAeBB4EHMQcxB8EGwQY9Bj0GuAW4BTYFNgW5BLkEPgQ+BL8DvwM9Az0DvQK9AkICQgLBAcEBKwErAXMAcwCM/4z/e/57/lH9Uf0a/Br83frd+qr5qvmJ+In4iPeI98P2w/ZL9kv2H/Yf9jn2OfaD9oP24fbh9k73TvfD98P3O/g7+Lj4uPgu+S75lPmU+e757vlJ+kn6uvq6+lb7Vvsn/Cf8Jv0m/UP+Q/51/3X/tgC2AAUCBQJfA18DtwS3BOoF6gXcBtwGfAd8B8cHxwfWB9YHxQfFB58HnwdnB2cHHgceB8AGwAZiBmIGIgYiBgkGCQYPBg8GEQYRBuUF5QVyBXIFwgTCBPAD8AMSAxIDKgIqAigBKAH+//7/u/67/o/9j/2r/Kv8Kfwp/Pv7+/v5+/n7/fv9+wf8B/wy/DL8lvyW/C/9L/3S/dL9Rf5F/nD+cP5s/mz+cP5w/qj+qP4Y/xj/nP+c/wkACQBeAF4AwgDCAGIBYgFOAk4CXANcA0EEQQTHBMcE8QTxBOwE7ATtBO0EAQUBBQIFAgXBBMEENQQ1BIwDjAMPAw8D7ALsAhADEAM9Az0DNgM2A+8C7wKUApQCXAJcAlcCVwJcAlwCLgIuAq4BrgH6APoAXABcAA4ADgAUABQAPgA+AFIAUgBEAEQAPQA9AHQAdAD9AP0AsQGxAUUCRQKGAoYCeQJ5AlMCUwJLAksCaQJpAoUChQJuAm4CGwIbArEBsQFvAW8BdQF1AawBrAHUAdQBuwG7AVoBWgHZANkAYgBiAP////+L/4v/3P7c/u397f3j/OP8+/v7+2D7YPsJ+wn7yfrJ+oD6gPou+i768/nz+ff59/k4+jj6j/qP+tD60Prm+ub65Prk+vn6+fpC+0L7tfu1+zX8Nfyq/Kr8G/0b/av9q/18/nz+jv+O/74AvgDhAeEB3gLeArkDuQOMBIwEZAVkBSsGKwbGBsYGJQclB04HTgdpB2kHnAecB+cH5wc4CDgIdgh2CI0IjQiNCI0IkAiQCJYIlgiQCJAIYQhhCPIH8gdNB00HkwaTBt0F3QUwBTAFgQSBBL0DvQPjAuMCDAIMAlEBUQG8ALwAQABAAMT/xP82/zb/mP6Y/gD+AP59/X39Cv0K/Zn8mfwX/Bf8g/uD+/b69vqL+ov6SvpK+jP6M/ow+jD6Lvou+jP6M/pH+kf6bvpu+qP6o/rR+tH64/rj+tr62vrC+sL6q/qr+qT6pPqn+qf6o/qj+pf6l/qF+oX6dvp2+nj6ePqH+of6kvqS+pP6k/qD+oP6Zvpm+kz6TPo3+jf6IPog+gT6BPrf+d/5tfm1+Zj5mPmS+ZL5ovmi+cj5yPn9+f35PPo8+ov6i/ro+uj6TvtO+7j7uPsd/B38evx6/NP80/wv/S/9kv2S/f39/f1x/nH+6/7r/mr/av/w//D/ewB7AAYBBgGLAYsBBwIHAngCeALfAt8CPQM9A4oDigPFA8UD7QPtAwAEAAQFBAUEAAQABO8D7wPSA9IDqgOqA3oDegNHA0cDFQMVA+AC4AKiAqICVwJXAv8B/wGcAZwBOAE4AdcA1wB5AHkAHQAdAMP/w/9x/3H/K/8r//n++f7Y/tj+wv7C/rP+s/6q/qr+qf6p/rX+tf7Q/tD+8/7z/hj/GP89/z3/Yv9i/4r/iv+5/7n/6//r/xwAHABKAEoAdwB3AKUApQDZANkADgEOAT0BPQFeAV4BbgFuAW4BbgFjAWMBUAFQATQBNAEMAQwB2gDaAKAAoABlAGUALQAtAPj/+P/A/8D/gf+B/z//P//8/vz+vf69/oT+hP5L/kv+EP4Q/tf91/2j/aP9fP18/Wb9Zv1c/Vz9V/1X/Vj9WP1i/WL9e/17/ar9qv3q/er9Mf4x/nf+d/63/rf+9f71/jX/Nf97/3v/wP/A//////8zADMAYQBhAJAAkADIAMgACQEJAUsBSwGFAYUBtwG3AeIB4gELAgsCNgI2AlsCWwJzAnMCewJ7AnQCdAJmAmYCWQJZAk8CTwJFAkUCOQI5AioCKgIeAh4CHgIeAioCKgJBAkECXQJdAncCdwKNAo0CpQKlAr8CvwLaAtoC9AL0AgYDBgMRAxEDGgMaAyUDJQM3AzcDUANQA2kDaQN/A38DkQORA54DngOqA6oDtgO2A74DvgO8A7wDrgOuA5IDkgNrA2sDPAM8AwgDCAPNAs0CjAKMAkICQgLwAfABlwGXAToBOgHbANsAfAB8AB0AHQC9/73/Wv9a//f+9/6U/pT+Nf41/t/93/2S/ZL9TP1M/Q79Dv3Y/Nj8rfyt/JP8k/yL/Iv8kfyR/KH8ofy5/Ln81vzW/P/8//w3/Tf9fv1+/c79zv0g/iD+b/5v/rr+uv4I/wj/XP9c/7P/s/8IAAgAUQBRAIoAigC2ALYA3gDeAAQBBAEpASkBRwFHAVYBVgFYAVgBVAFUAVEBUQFSAVIBVAFUAU0BTQE4ATgBFgEWAe8A7wDLAMsAqgCqAIgAiABdAF0AJwAnAOz/7P+5/7n/lf+V/4H/gf90/3T/Zv9m/1b/Vv9K/0r/S/9L/1//X/+C/4L/q/+r/9H/0f/z//P/FwAXAEUARQCBAIEAxADEAAUBBQE/AT8BcgFyAaUBpQHeAd4BHgIeAlkCWQKGAoYCoAKgAqgCqAKmAqYCoAKgApMCkwJ7AnsCUAJQAhICEgLGAcYBcwFzAR4BHgHHAMcAZwBnAP3//f+H/4f/D/8P/5/+n/49/j3+5/3n/Zv9m/1U/VT9D/0P/dT81Pyl/KX8g/yD/G78bvxh/GH8V/xX/FX8Vfxd/F38cvxy/Jf8l/zK/Mr8BP0E/Ub9Rv2Q/ZD94v3i/T/+P/6n/qf+Fv8W/4T/hP/u/+7/UABQAKkAqQD8APwASAFIAYoBigG/Ab8B5wHnAQICAgIVAhUCJQIlAjUCNQJCAkICSgJKAkUCRQIyAjICEwITAusB6wG8AbwBhQGFAUQBRAH2APYAnQCdAEEAQQDo/+j/l/+X/0//T/8M/wz/y/7L/pD+kP5f/l/+P/4//jL+Mv4y/jL+Nv42/jz+PP5E/kT+Uv5S/mz+bP6S/pL+vv6+/un+6f4Q/xD/Of85/2n/af+l/6X/6f/p/ysAKwBjAGMAjwCPALIAsgDVANUA+gD6ABwBHAE2ATYBQwFDAUQBRAFAAUABPwE/AT4BPgE4ATgBKAEoAQoBCgHmAOYAwwDDAKYApgCMAIwAbwBvAEkASQAcABwA8v/y/9H/0f+9/73/rv+u/5v/m/+A/4D/YP9g/0P/Q/8v/y//Jf8l/x7/Hv8U/xT/B/8H//3+/f7+/v7+Dv8O/yj/KP9F/0X/X/9f/3n/ef+W/5b/uv+6/+P/4/8JAAkAJQAlADYANgA/AD8ASQBJAFUAVQBhAGEAZwBnAGIAYgBVAFUASgBKAEgASABNAE0AVABUAFMAUwBDAEMAJgAmAAIAAgDd/93/tv+2/4z/jP9b/1v/Jv8m//P+8/7L/sv+r/6v/qH+of6a/pr+mP6Y/pv+m/6l/qX+tv62/s3+zf7j/uP+9f71/gL/Av8P/w//Hf8d/y7/Lv9B/0H/Uf9R/1//X/9w/3D/iP+I/6v/q//W/9b/AwADACwALABNAE0AZABkAHMAcwB4AHgAdAB0AGUAZQBPAE8ANQA1AB4AHgAMAAwAAQABAPj/+P/x//H/7f/t/+z/7P/w//D/9f/1//j/+P/0//T/6P/o/9X/1f++/77/p/+n/5D/kP94/3j/X/9f/0r/Sv8+/z7/Pv8+/03/Tf9o/2j/if+J/6z/rP/N/83/7P/s/wcABwAbABsAKQApADIAMgA5ADkAQwBDAFAAUABhAGEAdAB0AIQAhACRAJEAmACYAJsAmwCbAJsAlgCWAI4AjgB/AH8AaQBpAEkASQAdAB0A5v/m/6f/p/9q/2r/Nf81/xD/EP/7/vv+8v7y/vT+9P7//v/+Ff8V/zX/Nf9e/17/if+J/67/rv/J/8n/3f/d//L/8v8PAA8ANwA3AGcAZwCaAJoAzADMAP0A/QAwATABZwFnAZ8BnwHSAdIB/gH+ASACIAI+Aj4CWwJbAnQCdAKDAoMCgQKBAmsCawJGAkYCHAIcAvUB9QHPAc8BqAGoAXcBdwE6AToB9AD0AKwArABhAGEAEwATALz/vP9c/1z/9P70/pL+kv46/jr+8f3x/bb9tv2F/YX9XP1c/T/9P/0w/TD9Lf0t/TP9M/07/Tv9Q/1D/VL9Uv1s/Wz9lf2V/cv9y/0F/gX+PP48/m/+b/6o/qj+7v7u/kX/Rf+n/6f/CgAKAGUAZQC2ALYABAEEAVEBUQGdAZ0B4QHhAQ8CDwInAicCLQItAiwCLAIuAi4CNgI2AjsCOwI1AjUCIQIhAgMCAwLiAeIBxwHHAa0BrQGOAY4BYwFjASsBKwHtAO0AsQCxAH8AfwBUAFQAKgAqAPv/+//J/8n/mf+Z/3P/c/9c/1z/Tv9O/0P/Q/8z/zP/Hv8e/wv/C/8C/wL/CP8I/xr/Gv8x/zH/Rv9G/1j/WP9r/2v/hv+G/63/rf/d/93/DQANADQANABOAE4AXgBeAGgAaAB0AHQAgQCBAIoAigCLAIsAggCCAHIAcgBjAGMAWQBZAFMAUwBNAE0APgA+ACYAJgAHAAcA6P/o/83/zf+z/7P/l/+X/3X/df9Q/1D/MP8w/x3/Hf8a/xr/Iv8i/yv/K/8w/zD/Mf8x/zX/Nf9C/0L/V/9X/2z/bP94/3j/d/93/2v/a/9f/1//XP9c/2T/ZP9z/3P/hP+E/5X/lf+q/6r/yf/J//L/8v8dAB0AQQBBAFcAVwBhAGEAZgBmAG8AbwB9AH0AiwCLAJAAkACLAIsAgACAAHkAeQB8AHwAiQCJAJQAlACUAJQAhQCFAGwAbABQAFAAOAA4ACEAIQAFAAUA3P/c/6n/qf9y/3L/Qv9C/yT/JP8W/xb/Fv8W/x3/Hf8q/yr/Pv8+/1v/W/9//3//o/+j/8L/wv/X/9f/5P/k//D/8P//////EgASACcAJwA7ADsATQBNAGAAYAB5AHkAmwCbAMIAwgDsAOwAEwETATMBMwFJAUkBWAFYAVwBXAFWAVYBRgFGAS0BLQEOAQ4B7QDtAMwAzACsAKwAjgCOAHMAcwBbAFsARwBHADIAMgAYABgA8//z/8T/xP+P/4//Xv9e/zj/OP8g/yD/Ev8S/wv/C/8J/wn/EP8Q/yb/Jv9P/0//hv+G/8L/wv/4//j/JgAmAE4ATgB1AHUAmwCbALsAuwDSANIA3QDdAOMA4wDvAO8ACgEKATIBMgFiAWIBkwGTAcIBwgH1AfUBMgIyAnQCdAKvAq8C0gLSAtAC0AKtAq0CewJ7AkkCSQIfAh8C+AH4AcgByAGKAYoBRAFEAQIBAgHQANAArACsAIgAiABWAFYAEQARAMP/w/94/3j/Of85/wb/Bv/T/tP+lv6W/lb+Vv4f/h/+/v3+/fn9+f0G/gb+Ff4V/h/+H/4o/ij+Ov46/l/+X/6Q/pD+vP68/tP+0/7N/s3+tv62/qb+pv6w/rD+2/7b/h3/Hf9p/2n/tP+0//3//f9KAEoAnQCdAPMA8wBBAUEBfQF9AaABoAGtAa0BrwGvAasBqwGkAaQBmwGbAYsBiwF0AXQBVwFXATkBOQEeAR4BBwEHAfQA9ADkAOQA1ADUAMAAwAClAKUAggCCAFcAVwAnACcA9P/0/7//v/+L/4v/Wf9Z/y//L/8R/xH/Af8B//3+/f7+/v7++/77/vL+8v7j/uP+0P7Q/r3+vf6s/qz+mv6a/of+h/51/nX+aP5o/mX+Zf5u/m7+g/6D/p/+n/7A/sD+4v7i/gT/BP8m/yb/R/9H/2b/Zv+B/4H/l/+X/6b/pv+s/6z/qP+o/5z/nP+N/43/g/+D/4T/hP+S/5L/pv+m/7v/u//J/8n/z//P/87/zv/I/8j/uv+6/6T/pP+D/4P/XP9c/zT/NP8R/xH/8f7x/tL+0v6v/q/+hv6G/mL+Yv5K/kr+Pv4+/jz+PP42/jb+JP4k/gz+DP74/fj99v32/Qn+Cf4q/ir+S/5L/mX+Zf59/n3+oP6g/tj+2P4n/yf/g/+D/97/3v8vAC8AeQB5AMEAwQAKAQoBUQFRAYwBjAG4AbgB2QHZAfcB9wEdAh0CTAJMAngCeAKYApgCqQKpArICsgK8ArwC0QLRAugC6AL0AvQC6ALoAr8CvwKDAoMCQQJBAgACAAK/Ab8BeQF5AScBJwHJAMkAZwBnAAkACQCz/7P/ZP9k/xj/GP/M/sz+gv6C/j/+P/4E/gT+0P3Q/aT9pP2A/YD9Zv1m/V79Xv1q/Wr9iP2I/bf9t/3y/fL9NP40/nz+fP7H/sf+Ef8R/1T/VP+Q/5D/xv/G//n/+f8rACsAWgBaAIEAgQCcAJwArACsALUAtQC7ALsAwADAAMAAwAC2ALYAnwCfAIAAgABeAF4AQABAACcAJwAMAAwA7P/s/8T/xP+Z/5n/cf9x/1H/Uf81/zX/GP8Y//b+9v7Q/tD+qf6p/or+iv51/nX+Zv5m/ln+Wf5N/k3+SP5I/lH+Uf5t/m3+mf6Z/s/+z/4H/wf/PP88/3D/cP+p/6n/6P/o/ygAKABmAGYAngCeANAA0AAFAQUBPwE/AXsBewG2AbYB5gHmAQoCCgIiAiICOAI4AkoCSgJUAlQCUgJSAjoCOgIOAg4C1gHWAZgBmAFWAVYBEQERAcMAwwBvAG8AHAAcANf/1/+n/6f/i/+L/3r/ev9s/2z/X/9f/1r/Wv9i/2L/ef95/5f/l/+s/6z/r/+v/6T/pP+a/5r/ov+i/8T/xP/6//r/MQAxAFkAWQBuAG4AeQB5AIkAiQCmAKYAzgDOAPMA8wAFAQUB/gD+AOUA5QDFAMUAqQCpAJUAlQCDAIMAaABoAEMAQwAVABUA5//n/8D/wP+j/6P/i/+L/3L/cv9W/1b/OP84/yH/If8V/xX/Ef8R/w//D/8G/wb/8/7z/t3+3f7N/s3+zv7O/uP+4/4G/wb/Kv8q/0f/R/9e/17/df91/5j/mP/O/87/FQAVAGQAZACtAK0A6gDqABoBGgFEAUQBcgFyAacBpwHfAd8BFAIUAjoCOgJQAlACWAJYAlgCWAJaAloCXgJeAl8CXwJUAlQCOAI4AgoCCgLTAdMBnQGdAWsBawE7ATsBBQEFAcMAwwB4AHgAMAAwAPj/+P/T/9P/vP+8/6f/p/+L/4v/bP9s/1b/Vv9R/1H/Xv9e/23/bf9w/3D/X/9f/0X/Rf8z/zP/Ov86/1f/V/95/3n/iP+I/3z/fP9c/1z/Pv8+/zL/Mv8//z//Xf9d/4L/gv+v/6//6//r/zwAPACdAJ0A+QD5ADoBOgFVAVUBVQFVAVABUAFZAVkBcgFyAYsBiwGRAZEBfQF9AVsBWwE9AT0BLwEvASgBKAETARMB3wDfAI0AjQAuAC4A2//b/6H/of98/3z/XP9c/zT/NP8E/wT/2P7Y/r3+vf65/rn+wv7C/s7+zv7X/tf+3/7f/u7+7v4H/wf/Jf8l/z7/Pv9H/0f/Pf89/yT/JP8G/wb/7v7u/uP+4/7r/uv+Cf8J/zj/OP9u/27/nP+c/7b/tv+6/7r/sv+y/67/rv+2/7b/xv/G/8//z//H/8f/sv+y/6H/of+r/6v/1v/W/xgAGABZAFkAhwCHAKAAoACyALIAzQDNAPYA9gAhASEBQAFAAUcBRwE8ATwBLAEsASABIAERAREB9AD0ALwAvABrAGsAEAAQAL3/vf95/3n/QP9A/wr/Cv/V/tX+pv6m/or+iv6C/oL+h/6H/o7+jv6R/pH+mP6Y/q3+rf7Y/tj+Dv8O/z3/Pf9W/1b/Xv9e/2n/af+J/4n/wP/A//r/+v8dAB0AHgAeAAwADAAGAAYAJQAlAGQAZACjAKMAvgC+AKUApQBrAGsANAA0AB8AHwAvAC8ATgBOAGQAZABoAGgAaQBpAHoAegChAKEAzgDOAOMA4wDNAM0AkACQAEUARQAFAAUA2f/Z/7T/tP+B/4H/Nv82/+H+4f6X/pf+bv5u/mn+af55/nn+iv6K/pP+k/6V/pX+mP6Y/qD+oP6q/qr+q/6r/qT+pP6c/pz+nf6d/q/+r/7Q/tD+8/7z/g//D/8m/yb/QP9A/2f/Z/+b/5v/0v/S//v/+/8RABEAGQAZACgAKABPAE8AlACUAO4A7gBIAUgBkAGQAb4BvgHTAdMB2QHZAdUB1QHIAcgBsQGxAYsBiwFaAVoBIgEiAegA6ACwALAAfAB8AE4ATgAmACYACQAJAPv/+//8//z/CwALACIAIgA5ADkARABEADwAPAAbABsA4//j/57/nv9a/1r/Jf8l/wf/B/8C/wL/EP8Q/yn/Kf9L/0v/cv9y/5//n//J/8n/5P/k/+T/5P/F/8X/kv+S/17/Xv8//z//PP88/1D/UP9s/2z/g/+D/5L/kv+i/6L/vf+9/+b/5v8ZABkASQBJAGwAbAB+AH4AfwB/AG8AbwBNAE0AFQAVAMv/y/96/3r/M/8z/wX/Bf/2/vb+/v7+/hT/FP8z/zP/Xv9e/5r/mv/o/+j/OwA7AIAAgACnAKcAsACwAKoAqgCqAKoAuwC7ANYA1gDsAOwA7wDvAN8A3wDJAMkAvAC8ALsAuwC6ALoApgCmAHYAdgAwADAA7P/s/8X/xf/B/8H/1f/V/+n/6f/q/+r/2P/Y/8X/xf/K/8r/8f/x/zMAMwB2AHYAoACgAKgAqACVAJUAgQCBAH8AfwCVAJUAtgC2AM4AzgDOAM4AuAC4AJ4AngCRAJEAnACcALYAtgDMAMwAywDLAK4ArgB8AHwASwBLACgAKAAWABYADQANAP/////m/+b/xf/F/6X/pf+L/4v/eP94/2X/Zf9L/0v/Kf8p/wL/Av/b/tv+tP60/o3+jf5m/mb+QP5A/iX+Jf4d/h3+KP4o/kT+RP5l/mX+hf6F/qL+ov7A/sD+3/7f/gD/AP8f/x//PP88/2L/Yv+e/57/9//3/2gAaADdAN0AQQFBAYUBhQGxAbEB0wHTAfcB9wEXAhcCIAIgAvwB/AGoAagBOAE4AdIA0gCUAJQAhQCFAJEAkQCaAJoAjQCNAG4AbgBUAFQAUwBTAG8AbwCVAJUArACsAKQApACDAIMAXQBdAEcARwBHAEcAUwBTAFsAWwBWAFYASgBKAEkASQBiAGIAmwCbAOUA5QAsASwBXgFeAXQBdAFzAXMBaQFpAV8BXwFZAVkBUgFSAUIBQgEnAScBBwEHAekA6QDZANkA2wDbAOoA6gD6APoA+gD6AN8A3wClAKUAUgBSAPT/9P+Y/5j/R/9H/wX/Bf/M/sz+mP6Y/mn+af5D/kP+Mf4x/j3+Pf5t/m3+u/67/hX/Ff9n/2f/m/+b/6j/qP+S/5L/aP9o/zz/PP8X/xf//f79/uf+5/7T/tP+w/7D/sD+wP7V/tX+Bv8G/0v/S/+T/5P/yP/I/+H/4f/f/9//z//P/7//v/+3/7f/tv+2/7H/sf+i/6L/h/+H/2n/af9T/1P/Uf9R/2T/ZP+D/4P/pP+k/77/vv/P/8//2f/Z/+H/4f/o/+j/7P/s/+n/6f/k/+T/5f/l//f/9/8eAB4AUgBSAIIAggCcAJwAmACYAHsAewBTAFMALgAuABAAEADw//D/wf/B/4D/gP83/zf/+v76/tz+3P7n/uf+GP8Y/2H/Yf+y/7L/AQABAEoASgCLAIsAwwDDAPAA8AAQARABJQElATEBMQE3ATcBOwE7AT0BPQFAAUABSQFJAVcBVwFmAWYBbQFtAWEBYQE+AT4BDQENAd0A3QC3ALcAmwCbAHoAegBDAEMA8//z/5r/mv9U/1T/PP88/1f/V/+U/5T/2P/Y/wsACwAoACgANgA2AEAAQABEAEQAOQA5ABMAEwDU/9T/jP+M/1T/VP88/zz/P/8//0v/S/9I/0j/Lv8u/wf/B//m/ub+2v7a/uX+5f7+/v7+Gf8Z/zb/Nv9f/1//of+h//////9wAHAA5ADkAEsBSwGhAaEB6gHqASUCJQJOAk4CWQJZAjwCPAL9Af0BrgGuAWIBYgEiASIB4wDjAI8AjwAXABcAff99/9z+3P5V/lX++/37/cn9yf2p/an9gf2B/U/9T/0j/SP9Fv0W/Tj9OP2B/YH90v3S/RH+Ef4y/jL+Q/5D/lz+XP6R/pH+4P7g/jf/N/+A/4D/s/+z/9b/1v/9//3/NAA0AHwAfADJAMkAEwETAVYBVgGdAZ0B8AHwAVACUAKyArICBQMFAzkDOQNKA0oDPQM9AxgDGAPdAt0CjQKNAiYCJgKrAasBKgEqAawArAA5ADkAzf/N/2b/Zv8B/wH/of6h/k/+T/4P/g/+3v3e/bb9tv2R/ZH9cP1w/Vv9W/1b/Vv9cP1w/ZP9k/26/br93f3d/QD+AP4w/jD+d/53/tb+1v5E/0T/sv+y/xcAFwB2AHYA2ADYAEIBQgGvAa8BDQINAkcCRwJUAlQCQgJCAiUCJQIZAhkCKgIqAlACUAJzAnMCfgJ+AmUCZQIrAisC4wHjAZsBmwFYAVgBFgEWAcgAyABlAGUA7v/u/2//b//z/vP+gv6C/hz+HP62/bb9R/1H/dP80/xo/Gj8GPwY/Pf79/sI/Aj8Ovw6/Hn8efy1/LX85/zn/Bz9HP1k/WT9yP3I/UD+QP6+/r7+M/8z/5z/nP8IAAgAiACIACYBJgHcAdwBmAKYAj4DPgPBA8EDIAQgBGEEYQSLBIsEnwSfBJEEkQRZBFkE+AP4A3gDeAPtAu0CawJrAv0B/QGgAaABSwFLAfEA8QCFAIUABQAFAHv/e//1/vX+g/6D/jD+MP74/fj9zP3M/Zr9mv1X/Vf9B/0H/cD8wPyY/Jj8mPyY/Lz8vPzu/O78Gv0a/UD9QP1x/XH9w/3D/UP+Q/7m/ub+j/+P/yEAIQCTAJMA8gDyAFQBVAHLAcsBUwJTAtMC0wIyAzIDZQNlA3ADcANiA2IDRQNFAxUDFQPHAscCVwJXAs0BzQE9AT0BvAC8AFEAUQDu/+7/hP+E/wv/C/+G/ob+BP4E/pT9lP06/Tr97/zv/LD8sPx+/H78Yfxh/Gb8ZvyO/I78zvzO/Br9Gv1m/Wb9rf2t/fP98/07/jv+hP6E/s/+z/4e/x7/ev96/+r/6v9vAG8A/gD+AIUBhQH3AfcBUwJTAqICogLyAvICTgNOA64DrgMGBAYESwRLBHMEcwR7BHsEZgRmBC4ELgTQA9ADUANQA7cCtwIUAhQCcwFzAdoA2gBEAEQAqv+q/wz/DP9y/nL+5/3n/Xr9ev0w/TD9Bf0F/fj8+PwD/QP9HP0c/Tz9PP1Z/Vn9b/1v/Yf9h/2u/a798P3w/U7+Tv67/rv+JP8k/4D/gP/W/9b/OgA6ALgAuABNAU0B5AHkAWACYAK2ArYC7wLvAiMDIwNkA2QDswOzA/QD9AMLBAsE6APoA44DjgMSAxIDjgKOAg4CDgKVAZUBIQEhAbEAsQBDAEMA0v/S/1j/WP/M/sz+MP4w/pr9mv0m/Sb96Pzo/OH84fz2/Pb8BP0E/fj8+PzT/NP8qvyq/Jb8lvyj/KP8x/zH/Pb89vwn/Sf9Xv1e/ab9pv0F/gX+c/5z/uL+4v5K/0r/rP+s/xIAEgCJAIkADQENAZABkAECAgICXAJcAqECoQLZAtkCCgMKAyoDKgMsAywDCgMKA8sCywKCAoICRQJFAh0CHQIBAgEC4AHgAasBqwFcAVwB+gD6AJQAlAAyADIA2f/Z/4r/iv9F/0X/Cv8K/9j+2P6n/qf+bv5u/in+Kf7c/dz9kf2R/Vv9W/1H/Uf9Vf1V/YD9gP26/br9+v36/Tv+O/59/n3+vv6+/gD/AP9F/0X/kv+S/+3/7f9XAFcAzgDOAEIBQgGjAaMB5AHkAQECAQIFAgUC/wH/AfoB+gH5AfkB+wH7AfMB8wHeAd4BugG6AYsBiwFTAVMBFwEXAdoA2gCfAJ8AawBrAEUARQAtAC0AIAAgABYAFgACAAIA3f/d/6X/pf9d/13/DP8M/73+vf53/nf+PP48/g3+Df7m/eb9wv3C/ab9pv2Y/Zj9n/2f/cH9wf33/ff9M/4z/mL+Yv59/n3+i/6L/qD+oP7T/tP+K/8r/57/nv8RABEAbgBuAK0ArQDaANoACgEKAUcBRwGKAYoBwAHAAdUB1QHIAcgBrgGuAZ8BnwGoAagBxAHEAdcB1wHJAckBlQGVAUoBSgH+AP4AwgDCAJUAlQBnAGcAKgAqAN3/3f+K/4r/RP9E/xT/FP/z/vP+0P7Q/p7+nv5c/lz+EP4Q/sz9zP2Y/Zj9dv12/WL9Yv1a/Vr9Xf1d/XL9cv2g/aD96v3q/U/+T/7J/sn+UP9Q/9r/2v9eAF4A1ADUADsBOwGdAZ0BBwIHAn8CfwL+Av4CdAN0A8cDxwPrA+sD6APoA9AD0AO7A7sDswOzA6sDqwOIA4gDOQM5A7gCuAIVAhUCbQFtAdQA1ABTAFMA4v/i/3z/fP8Y/xj/uf65/mX+Zf4f/h/+4/3j/bH9sf2G/Yb9Y/1j/U39Tf1F/UX9Rf1F/UX9Rf07/Tv9JP0k/QX9Bf3u/O788Pzw/B39Hf2D/YP9Hf4d/tz+3P6l/6X/XABcAO8A7wBeAV4BtgG2AQUCBQJSAlIClQKVAr0CvQK9Ar0CmQKZAl8CXwIhAiEC6gHqAbYBtgF6AXoBMAEwAd8A3wCcAJwAeAB4AHcAdwCEAIQAgwCDAF4AXgAPAA8Aqf+p/0T/RP/x/vH+rv6u/m/+b/4k/iT+yv3K/Wz9bP0Z/Rn93fzd/Lj8uPyk/KT8lvyW/JX8lfyq/Kr84Pzg/Dz9PP2x/bH9Lf4t/p7+nv4A/wD/Wv9a/7n/uf8rACsArQCtADABMAGnAacBCAIIAlYCVgKfAp8C9AL0AlUDVQO5A7kDDQQNBDwEPAQ8BDwEFQQVBNID0gN/A38DJgMmA8MCwwJYAlgC6wHrAYQBhAEtAS0B5wDnAKkAqQBmAGYAFwAXAL//v/9m/2b/Fv8W/9L+0v6X/pf+X/5f/iz+LP4F/gX+8f3x/fX99f0N/g3+MP4w/ln+Wf6J/on+w/7D/gr/Cv9U/1T/lf+V/8P/w//g/+D/+v/6/xwAHABKAEoAeQB5AJcAlwCcAJwAlACUAJUAlQC3ALcA/wD/AFkBWQGlAaUBxgHGAbIBsgF0AXQBIAEgAccAxwBqAGoAAwADAI7/jv8K/wr/gv6C/gP+A/6Y/Zj9Q/1D/Qj9CP3k/OT80vzS/M/8z/zV/NX83Pzc/OP84/zv/O/8B/0H/TH9Mf1v/W/9uv26/RD+EP50/nT+7P7s/n3/ff8jACMAzQDNAGcBZwHgAeABNwI3AnMCcwKfAp8CwALAAs0CzQK/Ar8ClQKVAlYCVgIRAhEC0QHRAZUBlQFSAVIB/wD/AJ8AnwA/AD8A8f/x/8L/wv+s/6z/nP+c/37/fv9M/0z/EP8Q/+D+4P7V/tX+9/73/j7/Pv+X/5f/7P/s/zMAMwBvAG8AqQCpAOcA5wAnAScBXwFfAYMBgwGSAZIBlQGVAZsBmwGyAbIB2gHaAQYCBgIoAigCOAI4AjoCOgI+Aj4CUAJQAmsCawJ/An8CegJ6AlUCVQIZAhkC3AHcAa8BrwGSAZIBeQF5AVABUAENAQ0BtgC2AFgAWAD7//v/nP+c/zP/M/+2/rb+KP4o/pz9nP0j/SP9xvzG/IX8hfxW/Fb8Mvwy/B78Hvwl/CX8TfxN/JP8k/zq/Or8Q/1D/Zn9mf3w/fD9Tv5O/rj+uP4j/yP/hf+F/9X/1f8UABQATwBPAIwAjADOAM4ACwELATgBOAFRAVEBWwFbAWABYAFpAWkBdQF1AXoBegFtAW0BSgFKAREBEQHLAMsAfgB+ADEAMQDm/+b/nf+d/13/Xf8n/yf//f79/t7+3v7D/sP+pf6l/oX+hf5k/mT+S/5L/kD+QP5E/kT+UP5Q/mD+YP5u/m7+ff59/pb+lv7A/sD++v76/j//P/+D/4P/wv/C//////9LAEsAsQCxADEBMQG/Ab8BSAJIArcCtwIJAwkDRgNGA3UDdQObA5sDsQOxA6oDqgOCA4IDQQNBA/cC9wKyArICdAJ0AjQCNALlAeUBiAGIASUBJQHLAMsAhQCFAE0ATQATABMAyv/K/2//b/8H/wf/m/6b/jX+Nf7S/dL9bf1t/Qz9DP21/LX8cfxx/Ev8S/xB/EH8UPxQ/Hr8evzH/Mf8Ov06/dH90f18/nz+Jf8l/73/vf9FAEUAyADIAEwBTAHMAcwBMwIzAmoCagJuAm4CTgJOAiUCJQIKAgoC/gH+AfAB8AHNAc0BlAGUAVEBUQEZARkB9AD0ANYA1gCmAKYAUQBRANj/2P9Q/1D/z/7P/mX+Zf4L/gv+sP2w/U79Tv3p/On8k/yT/GH8Yfxb/Fv8dPx0/J/8n/zK/Mr87fzt/Av9C/0m/Sb9Qv1C/WL9Yv2K/Yr9wP3A/Qr+Cv5v/m/+7P7s/nv/e/8VABUAswCzAEoBSgHUAdQBTAJMAq4CrgL7AvsCNwM3A2MDYwN+A34DhwOHA3sDewNhA2EDRgNGAzUDNQMzAzMDOQM5AzUDNQMWAxYD1ALUAnICcgL4AfgBcQFxAeEA4QBKAEoAr/+v/xT/FP+A/oD+9v32/Xv9e/0Q/RD9ufy5/IP8g/x2/Hb8lPyU/Nr82vw8/Tz9rf2t/Sf+J/6s/qz+Of85/8j/yP9NAE0AuwC7AAwBDAFNAU0BjwGPAd8B3wE+Aj4CoQKhAvIC8gIkAyQDNgM2AywDLAMTAxMD8gLyAsoCygKbApsCZQJlAigCKALlAeUBnAGcAUsBSwHxAPEAkACQACsAKwDC/8L/U/9T/93+3f5a/lr+zf3N/T79Pv2z/LP8NPw0/Mf7x/tz+3P7P/s/+zn7Oftm+2b7w/vD+0L8QvzQ/ND8W/1b/d793v1g/mD+6/7r/ob/hv8sACwA1ADUAG0BbQHzAfMBaQJpAtIC0gI0AzQDjgOOA9UD1QMCBAIEEQQRBAQEBAThA+EDsQOxA3IDcgMfAx8DswKzAikCKQKFAYUB0QDRABkAGQBq/2r/y/7L/kH+Qf7M/cz9av1q/Rz9HP3h/OH8tvy2/Jr8mvyK/Ir8gvyC/If8h/yd/J38yvzK/BL9Ev11/XX97/3v/Xr+ev4U/xT/t/+3/10AXQACAQIBoQGhATYCNgLBAsECRANEA7kDuQMbBBsEYQRhBIIEggSABIAEZgRmBDoEOgQCBAIEugO6A1YDVgPPAs8CLgIuAoMBgwHeAN4ARgBGALP/s/8T/xP/Wf5Z/o/9j/3L/Mv8Jfwl/LL7svts+2z7RvtG+zT7NPs3+zf7V/tX+577nvsJ/An8g/yD/Pb89vxX/Vf9qf2p/QD+AP5w/nD+/f79/p7/nv9DAEMA4ADgAHIBcgEAAgACjQKNAhEDEQOAA4AD0QPRA/oD+gMCBAIE8wPzA84DzgOSA5IDOgM6A8UCxQI1AjUClQGVAfIA8gBSAFIAuf+5/yv/K/+r/qv+P/4//ur96v2r/av9ff19/Vv9W/1A/UD9K/0r/R/9H/0d/R39JP0k/Tf9N/1V/VX9f/1//bj9uP3//f/9Tf5N/p/+n/7z/vP+R/9H/53/nf/2//b/VgBWAL0AvQAxATEBtQG1AUUCRQLXAtcCXANcA8ADwAP/A/8DIQQhBDEEMQQ7BDsEQQRBBDQENAQEBAQErgOuAzcDNwOwArACJwInAqEBoQERAREBbQBtALX/tf/y/vL+O/47/qL9ov0w/TD92/zb/KD8oPx3/Hf8Y/xj/Gv8a/yP/I/8xvzG/Aj9CP1M/Uz9lP2U/eT95P1C/kL+qf6p/g//D/9v/2//xv/G/xsAGwB1AHUA1ADUAC4BLgF4AXgBrgGuAdIB0gH0AfQBHAIcAkcCRwJqAmoCeQJ5Am4CbgJQAlACLAIsAgYCBgLZAdkBmwGbAUUBRQHbANsAbQBtAA0ADQC8/7z/cf9x/x7/Hv+8/rz+UP5Q/u/97/2k/aT9bf1t/T79Pv0I/Qj9x/zH/JD8kPx5/Hn8kfyR/Nf81/w5/Tn9nv2e/QD+AP5l/mX+3P7c/mv/a/8KAAoAogCiABwBHAFyAXIBrwGvAeIB4gEXAhcCTAJMAnMCcwJ/An8CcgJyAlUCVQI5AjkCKQIpAiACIAISAhIC8wHzAb8BvwF5AXkBLAEsAeAA4ACYAJgAUgBSAA4ADgDK/8r/i/+L/1H/Uf8d/x3/6/7r/rv+u/6M/oz+Yf5h/j3+Pf4m/ib+Hv4e/iX+Jf45/jn+Vv5W/nX+df6V/pX+uP64/uX+5f4n/yf/gv+C/+//7/9kAGQA0QDRACoBKgFxAXEBrgGuAegB6AEjAiMCWAJYAnwCfAKIAogCfwJ/AmcCZwJJAkkCLAIsAgwCDALlAeUBsQGxAW4BbgEeAR4BxQDFAGUAZQD+//7/kP+Q/x3/Hf+o/qj+Nf41/sz9zP1y/XL9LP0s/QH9Af3y/PL8+vz6/Bb9Fv0+/T79aP1o/ZT9lP3B/cH98P3w/SH+If5U/lT+iP6I/rz+vP71/vX+NP80/33/ff/R/9H/LgAuAI4AjgDrAOsAPwE/AYQBhAG1AbUB0AHQAdcB1wHNAc0BtgG2AZQBlAFmAWYBLwEvAfAA8ACuAK4AbQBtADIAMgD7//v/yP/I/5v/m/92/3b/XP9c/0v/S/88/zz/J/8n/wr/Cv/p/un+0/7T/tP+0/7q/ur+D/8P/zT/NP9N/03/Xf9d/27/bv+M/4z/uv+6/+7/7v8cABwAPQA9AFIAUgBnAGcAgwCDAKUApQDGAMYA3wDfAO4A7gD9AP0AFQEVATkBOQFkAWQBjAGMAakBqQG9Ab0B0AHQAeYB5gH6AfoBBAIEAvoB+gHdAd0BtwG3AZQBlAF6AXoBZQFlAUgBSAEbARsB3QDdAJgAmABVAFUAHQAdAO//7//G/8b/nf+d/3L/cv9J/0n/I/8j/wX/Bf/z/vP+7v7u/vv++/4Y/xj/Qf9B/23/bf+V/5X/tv+2/9P/0//u/+7/CAAIABwAHAAhACEAEQARAPH/8f/L/8v/qv+q/5T/lP+E/4T/cP9w/0//T/8h/yH/7P7s/r3+vf6e/p7+kv6S/pj+mP6r/qv+xf7F/uL+4v4A/wD/HP8c/zT/NP9K/0r/X/9f/3T/dP+L/4v/ov+i/7f/t//J/8n/2v/a/+z/7P8EAAQAIQAhAEEAQQBfAF8AeQB5AIsAiwCYAJgAoQChAKoAqgCyALIAtwC3ALIAsgChAKEAgQCBAFgAWAArACsAAwADAOT/5P/M/8z/sf+x/43/jf9b/1v/If8h/+r+6v7A/sD+qf6p/qX+pf6s/qz+uP64/sX+xf7X/tf+8/7z/hz/HP9U/1T/mf+Z/+b/5v86ADoAjgCOAN8A3wAnAScBYwFjAZEBkQG3AbcB3AHcAQICAgIsAiwCUwJTAm8CbwJ7AnsCdgJ2AmQCZAJIAkgCJwInAvoB+gG8AbwBaQFpAQMBAwGSAJIAIQAhALn/uf9e/17/C/8L/8H+wf59/n3+Rv5G/iL+Iv4W/hb+If4h/j/+P/5o/mj+mP6Y/s/+z/4M/wz/TP9M/4z/jP/F/8X/8//z/xcAFwA3ADcAVwBXAHgAeACUAJQApgCmAKgAqACdAJ0AjACMAHsAewBuAG4AYwBjAFgAWABMAEwARgBGAEkASQBXAFcAagBqAHYAdgBwAHAAUgBSACIAIgDs/+z/uf+5/4//j/9s/2z/S/9L/yz/LP8Q/xD//P78/vP+8/7y/vL+9f71/vj++P76/vr+//7//gr/Cv8d/x3/M/8z/0r/Sv9d/13/cP9w/4b/hv+k/6T/zP/M//j/+P8kACQASgBKAGoAagCJAIkApwCnAMMAwwDWANYA2gDaAMwAzACwALAAkgCSAHsAewBtAG0AZABkAFgAWABCAEIAIwAjAAQABADr/+v/3f/d/9b/1v/N/83/vP+8/6T/pP+J/4n/dP90/2n/af9n/2f/af9p/27/bv93/3f/h/+H/6T/pP/L/8v/+f/5/yYAJgBPAE8AcwBzAJQAlAC0ALQA0gDSAOsA6wD+AP4ADQENARkBGQEpASkBOwE7AU8BTwFfAV8BagFqAW4BbgFtAW0BaAFoAVsBWwFCAUIBGwEbAeQA5ACcAJwASQBJAPH/8f+e/57/Vv9W/yL/Iv/+/v7+5P7k/sv+y/6q/qr+f/5//lX+Vf40/jT+Iv4i/iH+If4n/if+KP4o/iT+JP4e/h7+Iv4i/jj+OP5j/mP+nf6d/t3+3f4d/x3/Xf9d/57/nv/k/+T/MAAwAH8AfwDPAM8AGgEaAVwBXAGQAZABsAGwAbwBvAG1AbUBpQGlAZEBkQF6AXoBWwFbAS8BLwHwAPAAogCiAE4ATgD8//z/sP+w/2n/af8j/yP/3f7d/p7+nv5u/m7+Uf5R/kr+Sv5S/lL+ZP5k/n3+ff6a/pr+uv66/tn+2f71/vX+Cv8K/x3/Hf81/zX/Vf9V/3z/fP+l/6X/zP/M//H/8f8eAB4AWwBbAKoAqgAFAQUBYwFjAbgBuAEAAgACQgJCAn0CfQKxArEC2gLaAu4C7gLuAu4C4wLjAtEC0QK5ArkClQKVAlkCWQL/Af8BkAGQARsBGwGyALIAWABYAAoACgC8/7z/af9p/xf/F//R/tH+ov6i/oz+jP6J/on+j/6P/pn+mf6n/qf+vP68/tb+1v7v/u/+AP8A/wb/Bv8D/wP//f79/vf+9/7y/vL+6P7o/tz+3P7V/tX+3P7c/vj++P4l/yX/V/9X/4L/gv+k/6T/xv/G//X/9f85ADkAjACMAN0A3QAYARgBMwEzATIBMgEeAR4BBAEEAeYA5gDCAMIAkQCRAFMAUwAMAAwAxf/F/4b/hv9U/1T/Mf8x/x3/Hf8X/xf/HP8c/yn/Kf83/zf/QP9A/0T/RP9G/0b/Tv9O/1//X/95/3n/l/+X/7L/sv/G/8b/1f/V/+L/4v/x//H/AQABABAAEAAbABsAIgAiACMAIwAiACIAHwAfABwAHAAYABgAFQAVABcAFwAdAB0AKQApADcANwBGAEYAUwBTAF8AXwBrAGsAdQB1AH0AfQCAAIAAewB7AG8AbwBfAF8AUgBSAEoASgBJAEkATQBNAFYAVgBlAGUAewB7AJkAmQC+AL4A4gDiAP8A/wANAQ0BDQENAQIBAgH0APQA6gDqAOcA5wDoAOgA6gDqAOcA5wDfAN8A1ADUAMgAyAC4ALgApQClAIwAjABtAG0ATgBOADAAMAAWABYA+//7/93/3f+5/7n/kv+S/27/bv9R/1H/O/87/yn/Kf8V/xX//f79/uT+5P7P/s/+vv6+/rD+sP6g/qD+jf6N/nv+e/52/nb+iP6I/rb+tv74/vj+Q/9D/4r/iv/J/8n/AQABADcANwBtAG0AogCiAMwAzADoAOgA8gDyAO4A7gDdAN0AwwDDAJ8AnwB0AHQARgBGAB0AHQD9//3/5v/m/9H/0f+5/7n/l/+X/23/bf8//z//Ef8R/+f+5/7A/sD+m/6b/nj+eP5b/lv+R/5H/kL+Qv5N/k3+a/5r/p7+nv7m/ub+QP9A/6X/pf8LAAsAaABoALYAtgD0APQAIwEjAUYBRgFcAVwBZgFmAWEBYQFQAVABOQE5ASEBIQELAQsB+QD5AOkA6QDaANoAzgDOAMIAwgC1ALUAoACgAH4AfgBPAE8AFgAWAOD/4P+3/7f/nv+e/5T/lP+U/5T/lv+W/5n/mf+g/6D/r/+v/8j/yP/n/+f/BAAEABgAGAAhACEAIAAgABsAGwAZABkAHAAcACQAJAAwADAAPwA/AFAAUABlAGUAfQB9AJgAmAC0ALQAzADMAN0A3QDlAOUA5ADkANwA3ADPAM8AvgC+AKkAqQCOAI4AawBrAEEAQQASABIA4P/g/6//r/98/3z/Rf9F/wb/Bv+7/rv+ZP5k/gf+B/6t/a39Wv1a/Rj9GP3r/Ov80vzS/ND80Pzj/OP8B/0H/Tz9PP1//X/9y/3L/R/+H/56/nr+2/7b/kH/Qf+s/6z/HQAdAJEAkQAFAQUBdwF3AeMB4wFEAkQCmgKaAuAC4AIWAxYDPAM8A1EDUQNWA1YDTQNNAzIDMgMHAwcDzgLOAogCiAI6AjoC6QHpAZsBmwFUAVQBFAEUAdkA2QCcAJwAWwBbABIAEgDF/8X/eP94/zL/Mv/0/vT+vf69/on+if5X/lf+Jv4m/v/9//3q/er97f3t/Qv+C/4+/j7+fP58/r7+vv4A/wD/Qv9C/4j/iP/U/9T/JwAnAHoAegDLAMsAFQEVAVUBVQGNAY0BvwG/AekB6QEIAggCGwIbAhwCHAINAg0C8AHwAcgByAGWAZYBXQFdARoBGgHNAM0AdwB3AB4AHgDE/8T/bv9u/x//H//V/tX+i/6L/kL+Qv78/fz9uv26/YX9hf1f/V/9R/1H/T79Pv1C/UL9Tf1N/WH9Yf2A/YD9q/2r/eP94/0p/in+ef55/s/+z/4n/yf/fv9+/9L/0v8lACUAdwB3AMYAxgAOAQ4BTwFPAYQBhAGvAa8B0wHTAe8B7wEEAgQCEwITAhcCFwIRAhECAgICAu0B7QHUAdQBugG6AZ8BnwGBAYEBYQFhAUEBQQEiASIBBQEFAesA6wDQANAAtwC3AJ4AngCHAIcAdAB0AGEAYQBJAEkAKAAoAP7//v/P/8//of+h/33/ff9n/2f/X/9f/2D/YP9m/2b/bv9u/3n/ef+I/4j/nf+d/7f/t//T/9P/6//r//v/+/8CAAIA/f/9/+7/7v/W/9b/uP+4/5r/mv98/3z/Yv9i/0v/S/81/zX/H/8f/wn/Cf/0/vT+4/7j/tb+1v7N/s3+xv7G/sP+w/7E/sT+zP7M/tz+3P70/vT+D/8P/yn/Kf8//z//Uv9S/2T/ZP95/3n/kv+S/6z/rP/D/8P/0v/S/9r/2v/f/9//5f/l//D/8P8BAAEAFQAVACoAKgA8ADwATQBNAFwAXABpAGkAcQBxAHEAcQBrAGsAYABgAFYAVgBRAFEAUQBRAFIAUgBRAFEATABMAEMAQwA8ADwAOQA5AD0APQBDAEMASABIAEgASABCAEIAOwA7ADUANQAvAC8AKgAqACUAJQAfAB8AHQAdACIAIgAxADEASQBJAGQAZAB+AH4AkwCTAKIAogCuAK4AuQC5AMEAwQDHAMcAyQDJAMUAxQC+AL4AtQC1AKsAqwCiAKIAmQCZAJAAkACHAIcAfAB8AG8AbwBiAGIAVQBVAEkASQA/AD8ANAA0ACkAKQAaABoACgAKAPn/+f/q/+r/3P/c/87/zv+6/7r/nv+e/3r/ev9S/1L/LP8s/wz/DP/x/vH+1/7X/rz+vP6f/p/+g/6D/nH+cf5u/m7+ev56/pP+k/62/rb+2/7b/gH/Af8m/yb/Sv9K/2v/a/+K/4r/qf+p/8f/x//l/+X/AQABABkAGQAsACwAOwA7AEkASQBXAFcAZwBnAHYAdgCAAIAAhACEAIIAggB9AH0AegB6AHcAdwB1AHUAcQBxAGoAagBhAGEAWgBaAFQAVABRAFEATQBNAEUARQA2ADYAIAAgAAQABADk/+T/wP/A/5z/nP95/3n/Wv9a/0D/QP8u/y7/If8h/xn/Gf8Z/xn/Iv8i/zb/Nv9U/1T/ev96/6L/ov/H/8f/6P/o/wYABgAkACQARQBFAGYAZgCHAIcApwCnAMQAxADeAN4A+AD4AA0BDQEdAR0BJgEmAScBJwEhASEBFwEXAQgBCAH1APUA3wDfAMYAxgCuAK4AmgCaAIsAiwCAAIAAdwB3AG4AbgBkAGQAWgBaAFAAUABEAEQANwA3ACYAJgASABIA+//7/+P/4//L/8v/tv+2/6j/qP+i/6L/p/+n/7b/tv/L/8v/4//j//r/+v8QABAAJwAnAEEAQQBeAF4AewB7AJIAkgCiAKIAqgCqAKsAqwCqAKoApwCnAKAAoACSAJIAewB7AFsAWwA0ADQACAAIANr/2v+r/6v/fP98/0//T/8o/yj/Cv8K//f+9/7s/uz+5/7n/uP+4/7i/uL+4v7i/ur+6v75/vn+D/8P/yn/Kf9C/0L/VP9U/1//X/9l/2X/av9q/3T/dP+J/4n/qf+p/9P/0/8AAAAAKwArAFIAUgB1AHUAmACYALwAvADfAN8A/AD8AAsBCwELAQsBAAEAAe8A7wDfAN8A0QDRAMIAwgCvAK8AlACUAHQAdABTAFMAMwAzABUAFQD2//b/0v/S/6n/qf+A/4D/W/9b/zz/PP8k/yT/Dv8O//r++v7o/uj+3P7c/tz+3P7p/un+AP8A/xz/HP86/zr/Wf9Z/33/ff+o/6j/2v/a/w8ADwBCAEIAbgBuAJEAkQCuAK4AyADIAOEA4QD2APYABwEHAQ0BDQEJAQkB/AD8AOcA5wDNAM0AsACwAJIAkgByAHIAUABQACwALAAHAAcA4//j/8L/wv+o/6j/k/+T/4X/hf96/3r/bf9t/17/Xv9O/07/Qf9B/zz/PP9A/0D/Sv9K/1f/V/9k/2T/cP9w/3z/fP+N/43/o/+j/7//v//d/93/+v/6/xUAFQAzADMAVABUAHoAegCjAKMAyQDJAOkA6QACAQIBFwEXASoBKgE+AT4BUQFRAV0BXQFhAWEBXQFdAVIBUgFEAUQBNQE1ASIBIgEHAQcB4QDhALEAsQB8AHwASgBKACAAIAD7//v/2P/Y/7P/s/+K/4r/YP9g/z3/Pf8k/yT/Gf8Z/xj/GP8e/x7/Jf8l/yz/LP81/zX/Qv9C/1L/Uv9l/2X/df91/4L/gv+K/4r/kP+Q/5n/mf+l/6X/s/+z/8H/wf/M/8z/0v/S/9b/1v/b/9v/5f/l//b/9v8JAAkAGgAaACYAJgAsACwALgAuADAAMAA0ADQAOQA5AD0APQA8ADwANwA3AC8ALwApACkAJAAkACIAIgAdAB0AEwATAAQABADx//H/3f/d/8z/zP+//7//tP+0/6v/q/+h/6H/mP+Y/4//j/+J/4n/h/+H/4r/iv+Q/5D/mP+Y/6D/oP+n/6f/rP+s/7P/s/++/77/zv/O/+T/5P/9//3/FgAWAC0ALQBCAEIAVgBWAGoAagB7AHsAhQCFAIQAhAB1AHUAWQBZADQANAAMAAwA4//j/7r/uv+Q/5D/ZP9k/zr/Ov8W/xb/+P74/uP+4/7T/tP+x/7H/r7+vv68/rz+wv7C/tH+0f7o/uj+Af8B/xj/GP8s/yz/P/8//1X/Vf9y/3L/lf+V/7r/uv/e/97//////xwAHAA3ADcAUwBTAG8AbwCJAIkAngCeAK4ArgC5ALkAwgDCAMsAywDWANYA4QDhAOwA7ADyAPIA8wDzAO0A7QDhAOEA0ADQALoAugChAKEAhACEAGQAZABCAEIAHQAdAPn/+f/X/9f/uv+6/6X/pf+Y/5j/lP+U/5b/lv+d/53/qf+p/7r/uv/R/9H/7f/t/wwADAAqACoARwBHAF8AXwB2AHYAkACQAK0ArQDMAMwA6wDrAAQBBAEXARcBJAEkAS4BLgE3ATcBQAFAAUQBRAFCAUIBOAE4AScBJwEQARAB9wD3ANsA2wC8ALwAmgCaAHcAdwBSAFIALQAtAAcABwDe/97/sv+y/4X/hf9Y/1j/L/8v/wz/DP/t/u3+0P7Q/rT+tP6a/pr+hf6F/nv+e/59/n3+i/6L/qL+ov7A/sD+4/7j/gr/Cv83/zf/af9p/6D/oP/Y/9j/EQARAEUARQB0AHQAnACcAL0AvQDXANcA7QDtAP8A/wAKAQoBDwEPAQsBCwEAAQAB7wDvAN4A3gDPAM8AwwDDALgAuACtAK0AoACgAJEAkQCBAIEAcQBxAF8AXwBIAEgAKwArAAYABgDb/9v/qv+q/3f/d/9E/0T/Ef8R/+H+4f61/rX+jf6N/m3+bf5U/lT+RP5E/kL+Qv5P/k/+af5p/o7+jv67/rv+6v7q/hr/Gv9M/0z/gf+B/7j/uP/u/+7/IAAgAEcARwBlAGUAegB6AIkAiQCSAJIAlQCVAJAAkACAAIAAagBqAFEAUQA4ADgAIAAgAAYABgDo/+j/yP/I/6r/qv+S/5L/g/+D/33/ff96/3r/eP94/3X/df90/3T/dv92/37/fv+N/43/n/+f/7L/sv/H/8f/3v/e//n/+f8XABcAOQA5AFwAXACAAIAApQClAM0AzQD3APcAIgEiAUwBTAF0AXQBlwGXAbQBtAHMAcwB4AHgAfAB8AH8AfwBBAIEAgMCAwL4AfgB4gHiAcEBwQGaAZoBcQFxAUcBRwEbARsB6QDpAK4ArgBuAG4ALwAvAPr/+v/R/9H/s/+z/5r/mv+A/4D/ZP9k/0r/Sv84/zj/MP8w/zL/Mv85/zn/P/8//0L/Qv9F/0X/Sf9J/1D/UP9Z/1n/Y/9j/23/bf94/3j/g/+D/47/jv+Z/5n/of+h/6X/pf+n/6f/qv+q/63/rf+y/7L/tv+2/7j/uP+7/7v/vv++/8P/w//J/8n/zf/N/9D/0P/R/9H/0//T/9f/1//c/9z/4P/g/9//3//b/9v/1v/W/9X/1f/a/9r/4//j/+3/7f/1//X//P/8/wUABQAUABQAKAAoAD4APgBQAFAAWwBbAGMAYwBrAGsAeAB4AIkAiQCaAJoApgCmAKwArACvAK8AswCzALkAuQC/AL8AwQDBALsAuwCuAK4AngCeAI4AjgB/AH8AcABwAF8AXwBMAEwAOAA4ACUAJQAVABUABwAHAPv/+//w//D/5//n/+D/4P/c/9z/1v/W/8z/zP+7/7v/pP+k/4z/jP91/3X/Yf9h/03/Tf84/zj/IP8g/wn/Cf/1/vX+5P7k/tb+1v7G/sb+s/6z/qD+oP6P/o/+hP6E/n/+f/5+/n7+ff59/nz+fP57/nv+f/5//on+if6Y/pj+qf6p/rj+uP7F/sX+0P7Q/t3+3f7r/uv++f75/gX/Bf8N/w3/Ff8V/x//H/8t/y3/Qf9B/1j/WP9y/3L/jv+O/6z/rP/N/83/7//v/w4ADgApACkAQQBBAFgAWABwAHAAiQCJAKEAoQCyALIAvQC9AMEAwQDDAMMAxwDHAMwAzADQANAA0QDRAMwAzADDAMMAuQC5ALIAsgCrAKsAogCiAJYAlgCHAIcAdQB1AGUAZQBWAFYARgBGADMAMwAdAB0AAwADAOr/6v/R/9H/uP+4/5//n/+G/4b/cP9w/2T/ZP9l/2X/cf9x/4P/g/+Y/5j/rP+s/8H/wf/f/9//BgAGADYANgBmAGYAkACQAK8ArwDDAMMA0QDRAN0A3QDoAOgA7gDuAOsA6wDeAN4AyQDJALAAsACVAJUAeQB5AFsAWwA4ADgAEQARAOn/6f/D/8P/oP+g/4L/gv9o/2j/Uf9R/0H/Qf84/zj/N/83/zz/PP9E/0T/TP9M/1T/VP9d/13/av9q/3z/fP+S/5L/q/+r/8P/w//a/9r/8//z/wwADAAoACgASABIAGoAagCQAJAAuQC5AOAA4AAEAQQBIgEiATgBOAFKAUoBWwFbAWsBawF4AXgBgAGAAX8BfwFzAXMBYQFhAUsBSwE2ATYBIAEgAQcBBwHnAOcAvwC/AJMAkwBmAGYAPgA+AB0AHQABAAEA6//r/9n/2f/P/8//zP/M/9T/1P/l/+X//v/+/xwAHAA+AD4AYwBjAIYAhgCnAKcAwwDDANkA2QDrAOsA+wD7AAkBCQEVARUBHgEeASQBJAEmASYBJwEnASQBJAEcARwBDgEOAfcA9wDbANsAuwC7AJsAmwB6AHoAVABUACcAJwDy//L/uP+4/37/fv9J/0n/HP8c//f+9/7U/tT+sv6y/pL+kv52/nb+YP5g/lL+Uv5L/kv+Sf5J/kv+S/5S/lL+Xv5e/nL+cv6Q/pD+uf65/ur+6v4h/yH/Wv9a/5H/kf/D/8P/8f/x/xwAHABIAEgAdAB0AJwAnAC5ALkAxwDHAMcAxwC8ALwArQCtAJ0AnQCKAIoAcgByAFAAUAAjACMA7f/t/7b/tv9//3//Sf9J/xX/Ff/e/t7+pf6l/m3+bf46/jr+Df4N/u397f3Z/dn9zv3O/cz9zP3R/dH92/3b/er96v0B/gH+If4h/k3+Tf6E/oT+wP7A/v7+/v47/zv/d/93/7f/t///////UwBTAK0ArQAIAQgBXAFcAaYBpgHnAecBJAIkAl8CXwKWApYCxwLHAusC6wICAwIDEAMQAxYDFgMXAxcDFwMXAxADEAMCAwID7ALsAs4CzgKqAqoChAKEAlwCXAI1AjUCDgIOAuYB5gG8AbwBlAGUAW0BbQFLAUsBLgEuARcBFwEGAQYB+gD6APMA8wDyAPIA9wD3AAIBAgEPAQ8BGQEZAR4BHgEeAR4BHAEcAR0BHQEiASIBLAEsATUBNQE5ATkBNwE3ATABMAEnAScBHgEeARQBFAEHAQcB8gDyANUA1QCzALMAjwCPAGwAbABMAEwALQAtAAsACwDk/+T/uf+5/43/jf9k/2T/Qv9C/yr/Kv8b/xv/Dv8O/wD/AP/t/u3+1v7W/sL+wv63/rf+uf65/sX+xf7W/tb+5P7k/u7+7v74/vj+B/8H/yL/Iv9F/0X/af9p/4j/iP+d/53/rf+t/77/vv/W/9b/9v/2/xcAFwA1ADUATABMAFwAXABtAG0AggCCAJoAmgCzALMAxwDHAM8AzwDNAM0AwwDDALUAtQClAKUAkwCTAH0AfQBgAGAAPQA9ABUAFQDs/+z/x//H/6f/p/+K/4r/bv9u/0//T/8r/yv/BP8E/+P+4/7M/sz+wf7B/r7+vv68/rz+sv6y/qH+of6O/o7+gP6A/n7+fv6G/ob+kf6R/pf+l/6V/pX+if6J/nz+fP5y/nL+bP5s/mv+a/5p/mn+Y/5j/lj+WP5J/kn+N/43/iX+Jf4Q/hD+9v32/df91/2u/a79ev16/T39Pf33/Pf8q/yr/GH8YfwY/Bj8z/vP+4z7jPtL+0v7C/sL+9T61Pqm+qb6gvqC+mz6bPpj+mP6Z/pn+nr6evqe+p76zvrO+gr7CvtR+1H7ofuh+/77/vtr/Gv85vzm/Gz9bP31/fX9fP58/v7+/v6A/4D/BgAGAI4AjgAWARYBmAGYAQsCCwJzAnMC1wLXAjUDNQOOA44D2gPaAwwEDAQbBBsECQQJBNUD1QN/A38DBQMFA2ACYAKIAYgBfgB+AE7/Tv/+/f79mPyY/Cn7Kfuw+bD5Mfgx+L/2v/Zl9WX1KvQq9B7zHvM+8j7ygfGB8fHw8fCQ8JDwXPBc8GbwZvCn8KfwEvES8abxpvFc8lzyKvMq8xb0FvQd9R31NPY09ln3WfeC+IL4p/mn+cb6xvrh++H79fz1/AP+A/4O/w7/FgAWABkBGQEXAhcCEQMRAwEEAQTsBOwE2QXZBcMGwwatB60HkwiTCGEJYQkSChIKpgqmChcLFwtxC3ELwQvBC/4L/gsqDCoMRAxEDEUMRQw4DDgMMAwwDDAMMAxDDEMMcAxwDKYMpgzlDOUMMw0zDYgNiA3yDfINfQ59DhgPGA+/D78PbRBtEAkRCRGWEZYRHhIeEpUSlRIBEwETXxNfE5MTkxOPE48TWBNYE+IS4hI9Ej0SehF6EZMQkxCHD4cPVg5WDvkM+Qx6C3oL8QnxCWsIawjxBvEGhAWEBR8EHwS6AroCWgFaAQkACQDQ/tD+sf2x/bD8sPy/+7/71/rX+vz5/Pkv+S/5dvh2+N733vdl92X3BfcF98H2wfaT9pP2dvZ29nH2cfaE9oT2rvau9vX29fZV91X3xffF90P4Q/jJ+Mn4U/lT+en56fmL+ov6OPs4++r76vuW/Jb8Lv0u/a39rf0V/hX+bv5u/rz+vP4A/wD/MP8w/0f/R/9B/0H/Jf8l/wH/Af/h/uH+zf7N/sX+xf7G/sb+zv7O/tz+3P76/vr+LP8s/3X/df/T/9P/QABAALMAswApASkBoQGhARYCFgKIAogC9gL2AloDWgOzA7MD/gP+AzgEOARdBF0EbgRuBGkEaQROBE4EIwQjBOkD6QOmA6YDXQNdAxEDEQPDAsMCdQJ1AicCJwLYAdgBigGKAT8BPwH3APcAsgCyAGwAbAAfAB8AxP/E/1r/Wv/k/uT+Zf5l/ub95v1n/Wf94/zj/Fz8XPzO+877Ofs5+636rfoy+jL6zfnN+YX5hflV+VX5M/kz+R/5H/kX+Rf5GfkZ+S75LvlZ+Vn5lPmU+d353fks+iz6dfp1+rn6ufr2+vb6Lvsu+2X7Zfua+5r7xPvE++H74fvq++r74fvh+9H70fvG+8b7xfvF+9b71vvz+/P7F/wX/EL8Qvx4/Hj8vvy+/Br9Gv2N/Y39Ef4R/pz+nP4n/yf/rP+s/yoAKgCgAKAAEQERAXYBdgHPAc8BFwIXAkwCTAJuAm4CgAKAAoQChAKBAoECewJ7AnQCdAJpAmkCWgJaAkECQQIgAiAC/wH/AeMB4wHSAdIBygHKAcIBwgGzAbMBmQGZAXUBdQFNAU0BJQElAf0A/QDOAM4AkwCTAEcARwDq/+r/gv+C/xf/F/+s/qz+Q/5D/uD94P2C/YL9K/0r/d783vye/J78a/xr/Er8Svw7/Dv8O/w7/En8Sfxe/F78c/xz/If8h/yd/J38t/y3/Nz83PwI/Qj9NP00/Vn9Wf12/Xb9kP2Q/bH9sf3j/eP9Kf4p/n3+ff7Z/tn+N/83/5b/lv/9//3/cQBxAO8A7wB1AXUB/wH/AYMCgwICAwIDfQN9A/ID8gNfBF8EwgTCBBEFEQVKBUoFbQVtBXMFcwVgBWAFNwU3BfoE+gSxBLEEYQRhBAoECgSrA6sDQwNDA9QC1AJgAmAC8AHwAYcBhwEkASQBxADEAGUAZQADAAMAov+i/0X/Rf/s/uz+k/6T/jr+Ov7d/d39ff19/SD9IP3J/Mn8dvx2/Cn8Kfze+977kvuS+037TfsP+w/71vrW+qX6pfp3+nf6SvpK+ib6JvoP+g/6A/oD+gj6CPoW+hb6KPoo+kL6Qvpl+mX6kvqS+sz6zPoM+wz7R/tH+3z7fPuo+6j7zfvN+/X79fsl/CX8Xvxe/KH8ofzt/O38P/0//Zb9lv32/fb9X/5f/tb+1v5h/2H//////6kAqQBYAVgBBwIHAq4CrgJWA1YDCAQIBMAEwAR5BXkFJQYlBqoGqgb8BvwGGAcYBwAHAAezBrMGMQYxBmsFawVKBEoEwwLDAtoA2gCX/pf+D/wP/Gn5afmy9rL29/P381bxVvHQ7tDuZexl7DjqOOpN6E3oq+ar5nPlc+Wi5KLkJeQl5ALkAuQl5CXkeeR55BLlEuX35fflIuci55/on+hZ6lnqL+wv7BPuE+797/3v6/Hr8evz6/MJ9gn2PPg8+Hj6ePqz/LP84f7h/vwA/AAUAxQDNQU1BVkHWQeKCYoJwgvCC+gN6A31D/UP6hHqEa0TrRNHFUcVwRbBFgAYABgDGQMZxRnFGSUaJRolGiUa3BncGUsZSxmVGJUY3BfcFx0XHRdhFmEWtxW3FRMVExWKFIoUNRQ1FBAUEBQmFCYUeRR5FOkU6RRuFW4VChYKFqYWphZKF0oX/hf+F6cYpxg+GT4ZvBm8Gf0Z/Rn8GfwZwBnAGTYZNhlmGGYYXBdcFwsWCxZyFHIUnRKdEosQixBIDkgO7gvuC4sJiwkqByoH2QTZBJ4CngJ7AHsAc/5z/o78jvzJ+sn6IPkg+Zr3mvcr9iv2zPTM9Ifzh/NV8lXyMfEx8SvwK/A/7z/vbu5u7s/tz+1j7WPtKe0p7S3tLe1n7Wfty+3L7WDuYO4m7ybvFPAU8DPxM/F98n3y4vPi81z1XPXk9uT2bvhu+Pz5/PmL+4v7Ev0S/YP+g/7T/9P/9AD0AN8B3wGUApQCGgMaA3MDcwOhA6EDqAOoA4cDhwNDA0MD6wLrAowCjAI3AjcC+QH5AdYB1gHRAdEB7AHsASMCIwJ6AnoC9QL1AokDiQMvBC8E3gTeBIMFgwUbBhsGqAaoBikHKQekB6QHHggeCIsIiwjpCOkINQk1CWgJaAmFCYUJlgmWCZMJkwl9CX0JVQlVCREJEQmxCLEIOwg7CK4HrgcSBxIHbQZtBr4FvgUEBQQFOwQ7BF8DXwNuAm4CaQFpAVcAVwA7/zv/Ff4V/un86fyy+7L7b/pv+i/5L/n89/z33/bf9u317fUk9ST1ffR99P7z/vOi86LzZ/Nn81zzXPOA84Dzx/PH8yz0LPSb9Jv0AvUC9WT1ZPXB9cH1GPYY9nX2dfbU9tT2L/cv94n3iffd9933Kfgp+HL4cvi4+Lj4+vj6+D/5P/mH+Yf5z/nP+Rj6GPpc+lz6l/qX+s/6z/oN+w37W/tb+8L7wvs8/Dz8u/y7/C/9L/2R/ZH94/3j/TT+NP6U/pT+CP8I/4j/iP8EAAQAbgBuAMAAwAAEAQQBTgFOAaoBqgEfAh8CqAKoAjIDMgOwA7ADHgQeBHsEewTRBNEEKQUpBX4FfgXGBcYF8gXyBfAF8AW4BbgFVgVWBdgE2ARRBFEE0APQA1MDUwPOAs4COAI4AowBjAHRANEAGAAYAHT/dP/q/ur+df51/g3+Df6l/aX9O/07/dr82vyM/Iz8WfxZ/EP8Q/xD/EP8TfxN/Fz8XPxq/Gr8efx5/Iz8jPyi/KL8uvy6/NH80fzi/OL87Pzs/O/87/zs/Oz85fzl/N/83/zb/Nv81/zX/NX81fzQ/ND8w/zD/K78rvyQ/JD8Z/xn/Dj8OPwF/AX8yvvK+437jftK+0r7/vr++q/6r/pf+l/6D/oP+sv5y/mT+ZP5aPlo+U75TvlC+UL5QPlA+U75Tvlu+W75oPmg+en56flJ+kn6t/q3+jL7Mvu4+7j7RPxE/Nz83PyB/YH9M/4z/u3+7f6r/6v/ZQBlABUBFQG7AbsBWwJbAvUC9QKLA4sDHQQdBKAEoAQRBREFbwVvBbgFuAXzBfMFKgYqBl0GXQaMBowGtwa3BtMG0wbhBuEG4wbjBtgG2AbEBsQGqgaqBoQGhAZTBlMGGwYbBtwF3AWcBZwFYgViBSsFKwX2BPYEwwTDBI4EjgRWBFYEHwQfBOcD5wOuA64DdgN2AzsDOwP9Av0CvAK8AncCdwIqAioC1gHWAXkBeQESARIBoACgACQAJACe/57/D/8P/3v+e/7n/ef9VP1U/cj8yPxE/ET8x/vH+1b7Vvv1+vX6ovqi+mX6Zfo5+jn6F/oX+gH6Afr5+fn5AvoC+iX6Jfpl+mX6uPq4+hj7GPt9+3374fvh+0n8Sfy8/Lz8O/07/cT9xP1Q/lD+1P7U/kv/S/+7/7v/KwArAKIAogAjASMBrQGtATUCNQK2ArYCLQMtA5UDlQPvA+8DPQQ9BHoEegSfBJ8EpQSlBH4EfgQdBB0EfAN8A5kCmQJ2AXYBGQAZAJD+kP7a/Nr88/rz+uX45fip9qn2R/RH9OPx4/GM74zvVe1V7WHrYeux6bHpPeg96BnnGec+5j7mpeWl5WnlaeWN5Y3lBuYG5t7m3uYA6ADoSelJ6bPqs+o07DTsxO3E7Xbvdu9S8VLxSvNK81L1UvVT91P3Ofk5+QL7AvvA/MD8iv6K/mwAbAByAnIClASUBLUGtQbNCM0I2QrZCssMywy1DrUOpBCkEIQShBJTFFMUBBYEFm4XbheJGIkYYRlhGe0Z7RlLGksalxqXGr4avhq6GroahBqEGgIaAhpFGUUZeBh4GKwXrBf/Fv8WfhZ+Fg8WDxavFa8VahVqFT4VPhVCFUIVghWCFdoV2hUzFjMWehZ6FpIWkhZ/Fn8WTxZPFvEV8RVeFV4VlhSWFIwTjBNLEksS5xDnEGIPYg+4DbgN6gvqC/AJ8AnSB9IHqAWoBYYDhgNwAXABZ/9n/2r9av14+3j7mvma+ez37Pd09nT2K/Ur9RT0FPQa8xrzM/Iz8mrxavG+8L7wJ/An8K7vru9J70nv8e7x7rvuu+6s7qzuvu6+7vvu++5b71vv1u/W73zwfPBW8VbxYfJh8p3znfP99P30cPZw9vT39PeN+Y35O/s7+/X89fyu/q7+TgBOAMQBxAENAw0DMQQxBC4FLgUJBgkGwAbABkkHSQepB6kH7QftBxkIGQg0CDQIQghCCDoIOggfCB8I/wf/B90H3QfFB8UHuQe5B68HrwenB6cHqwerB74HvgftB+0HOgg6CJIIkgjmCOYILAksCVYJVglmCWYJXwlfCTYJNgnpCOkIeQh5COkH6QdFB0UHnAacBvMF8wVKBUoFogSiBPsD+wNaA1oDwwLDAjMCMwKhAaEBAgECAVIAUgCV/5X/1/7X/if+J/6G/Yb97Pzs/FP8U/yw+7D7AfsB+1L6Uvqo+aj5BPkE+W74bvjg9+D3VfdV99r22vZw9nD2GPYY9t/13/XF9cX1yfXJ9fL18vU89jz2nvae9hP3E/eR95H3DvgO+I/4j/gT+RP5mPmY+Rz6HPqV+pX69vr2+jv7O/ti+2L7aPto+1f7V/sx+zH7+vr6+rz6vPqB+oH6UfpR+jr6OvpB+kH6Zfpl+qj6qPoE+wT7c/tz+/H78ft6/Hr8B/0H/Zb9lv0j/iP+rP6s/i3/Lf+l/6X/EgASAHQAdADNAM0AHAEcAVsBWwGGAYYBmAGYAZEBkQF6AXoBXwFfAUgBSAE3ATcBKQEpARYBFgH9AP0A5QDlANgA2ADcANwA8wDzAA8BDwEhASEBHAEcAfoA+gC9AL0AaABoAAAAAACE/4T/8v7y/k7+Tv6b/Zv94Pzg/Cv8K/yE+4T78Prw+nv6e/om+ib68fnx+eD54Pnt+e35FPoU+lb6Vvqz+rP6KPso+7H7sftG/Eb82fzZ/GL9Yv3Z/dn9Pf49/o3+jf7I/sj+6f7p/vD+8P7c/tz+sv6y/nr+ev46/jr+9P30/az9rP1i/WL9GP0Y/db81vyd/J38bfxt/Ef8R/wr/Cv8GPwY/Bj8GPws/Cz8U/xT/If8h/y8/Lz85/zn/AT9BP0T/RP9FP0U/Qz9DP37/Pv83/zf/L38vfyY/Jj8efx5/Gr8avxv/G/8ifyJ/Lf8t/zz/PP8N/03/X79fv3D/cP9Bf4F/kL+Qv57/nv+r/6v/tz+3P79/v3+D/8P/xL/Ev8G/wb/8P7w/tb+1v66/rr+mv6a/nn+ef5Z/ln+QP5A/jb+Nv5F/kX+cf5x/rz+vP4n/yf/rf+t/0oASgD6APoAuQG5AX8CfwJNA00DIwQjBPsE+wTTBdMFpgamBmYHZgcQCBAIpQilCB8JHwmECYQJ2AnYCREKEQoxCjEKPQo9CjMKMwodCh0KBAoECuIJ4gm2CbYJggmCCT8JPwnxCPEInQidCDwIPAjGB8YHOAc4B44GjgbMBcwFAAUABTEEMQRhA2EDkQKRArwBvAHgAOAAAwADAC3/Lf9k/mT+qf2p/f78/vxd/F38xfvF+z37PfvI+sj6aPpo+iX6Jfr/+f/59fn1+Qf6B/oz+jP6bPps+q36rfrw+vD6M/sz+337ffvP+8/7JPwk/HX8dfy2/Lb84Pzg/Pv8+/wV/RX9O/07/XX9df2+/b79CP4I/kv+S/6G/ob+wP7A/gb/Bv9d/13/xP/E/zEAMQCcAJwA/AD8AFEBUQGaAZoB2QHZAQwCDAIsAiwCMgIyAhQCFALFAcUBOgE6AXQAdABx/3H/Pf49/ur86vx3+3f74Png+Sn4KfhE9kT2NfQ19B7yHvIT8BPwJO4k7nLscuz16vXqo+mj6Y7ojui257bnGecZ59Tm1Obi5uLmMucy58rnyueb6JvokOmQ6a3qrert6+3rRu1G7cfux+568HrwW/Jb8m30bfSk9qT25fjl+B/7H/tU/VT9j/+P/9gB2AFCBEIExgbGBkUJRQmwC7AL/Q39DR4QHhApEikSMhQyFCMWIxb1F/UXnBmcGewa7BrlG+UbmhyaHAAdAB0uHS4dOx07HRQdFB2+HL4cRBxEHJIbkhu1GrUaxhnGGcQYxBjLF8sX9Rb1FjQWNBaJFYkV8RTxFFcUVxS+E74TOhM6E8QSxBJiEmISDRINEqYRphEZERkRZBBkEIAPgA95DnkOYw1jDTsMOwwBCwELsgmyCUcIRwjBBsEGKQUpBYcDhwPiAeIBPgA+AKP+o/4O/Q79f/t/+wL6AvqT+JP4NPc09/H18fXI9Mj0s/Oz88DywPLl8eXxHPEc8XPwc/Dm7+bvbu9u7xrvGu/n7ufu0O7Q7ubu5u4t7y3vpO+k71XwVfA+8T7xVfJV8p7znvMa9Rr1w/bD9pb4lviL+ov6kPyQ/JL+kv6IAIgAawJrAjEEMQTfBd8FcwdzB98I3wgeCh4KMAswCwkMCQyvDK8MLw0vDYMNgw21DbUN0g3SDdMN0w2/Db8Nng2eDWkNaQ0oDSgN5gzmDKQMpAxoDGgMOgw6DAcMBwzJC8kLfAt8CxgLGAukCqQKLwovCrYJtgk6CToJuQi5CCUIJQiAB4AH0AbQBhoGGgZkBWQFsgSyBP4D/gNEA0QDhAKEAsIBwgECAQIBSwBLAKL/ov8F/wX/cf5x/uP94/1T/VP9ufy5/BX8Ffxh+2H7nPqc+tD50Pn9+P34Jfgl+FP3U/eE9oT2tvW29ff09/RK9Er0sPOw8zjzOPPf8t/yn/Kf8oHygfKA8oDylfKV8svyy/Ij8yPzmfOZ8zT0NPTy9PL0xvXG9az2rPaY95j3ffh9+Fr5Wvkx+jH6AfsB+8v7y/uL/Iv8OP04/cz9zP1G/kb+p/6n/vf+9/45/zn/bP9s/4z/jP+T/5P/ff99/1H/Uf8b/xv/6f7p/sj+yP6+/r7+zP7M/ur+6v4Q/xD/M/8z/1D/UP9k/2T/cf9x/37/fv+L/4v/mP+Y/5//n/+a/5r/h/+H/2n/af9I/0j/Kf8p/w7/Dv/w/vD+wf7B/nj+eP4S/hL+mP2Y/R79Hv2w/LD8VPxU/An8CfzB+8H7bvtu+xP7E/u0+rT6WfpZ+hH6Efra+dr5qfmp+Xv5e/lI+Uj5DvkO+dn42fiw+LD4lfiV+I/4j/iX+Jf4p/in+ML4wvjp+On4HPkc+WX5ZfnH+cf5QfpB+tL60vp3+3f7Jvwm/Nv82/yS/ZL9R/5H/vz+/P6v/6//XQBdAP8A/wCLAYsB/QH9AU4CTgKBAoECmQKZApgCmAKBAoECVQJVAhUCFQLCAcIBYAFgAfIA8gB7AHsA/P/8/3r/ev/6/vr+f/5//hL+Ev6x/bH9W/1b/RD9EP3L/Mv8j/yP/GL8YvxK/Er8RfxF/FP8U/xs/Gz8hfyF/Jv8m/ys/Kz8u/y7/ND80Pzv/O/8FP0U/T79Pv1l/WX9hP2E/Zv9m/2w/bD9xP3E/dr92v3s/ez98f3x/eP94/29/b39hP2E/UP9Q/0D/QP9yfzJ/Jn8mfxu/G78Q/xD/Br8Gvz3+/f73vve+9j72Pvn++f7DPwM/Ev8S/yo/Kj8Jf0l/cT9xP2H/of+a/9r/2oAagCEAYQBtQK1Au8D7wMvBS8FcQZxBqQHpAfKCMoI5gnmCewK7ArjC+MLzQzNDJsNmw1ODk4O6Q7pDl0PXQ+uD64P4A/gD+kP6Q/OD84PnQ+dD1APUA/vDu8OhA6EDgIOAg5qDWoNwQzBDAAMAAwuCy4LUwpTCm4Jbgl9CH0IhgeGB4YGhgZ/BX8FegR6BHsDewOEAoQClgGWAbEAsQDT/9P//P78/jD+MP5w/XD9vfy9/Bz8HPyJ+4n7AfsB+4f6h/oY+hj6sfmx+Vf5V/kH+Qf5wPjA+In4ifhh+GH4RvhG+D34PfhG+Eb4WPhY+Hb4dvib+Jv4w/jD+PP48/gt+S35cvly+cb5xvkp+in6k/qT+gP7A/t4+3j78fvx+3L8cvwA/QD9mv2a/Tz+PP7m/ub+kv+S/z8APwDvAO8ApAGkAVgCWAIHAwcDrAOsAzkEOQSpBKkE9wT3BBwFHAUSBRIF1wTXBGQEZAS1A7UDxgLGApsBmwEtAC0AgP6A/qH8ofyQ+pD6U/hT+P/1//WU85TzFfEV8aXupe5O7E7sH+of6kPoQ+i85rzmg+WD5azkrOQq5Crk7uPu4wzkDOR+5H7kNeU15TjmOOZ653rn5Ojk6H7qfupI7EjsPO487mPwY/C58rnyL/Uv9bj3uPdK+kr61vzW/FL/Uv/HAccBOwQ7BKwGrAYmCSYJqQupCxsOGw54EHgQvBK8Es0UzRSyFrIWdRh1GPgZ+Bk4GzgbNRw1HNEc0RwXHRcdIh0iHe4c7hyRHJEcIRwhHI0bjRvaGtoaFhoWGjUZNRlIGEgYaBdoF44WjhbLFcsVMBUwFasUqxRAFEAU8BPwE6cTpxNlE2UTMhMyE/4S/hLGEsYShBKEEhwSHBKAEYARsxCzEK8Prw+BDoEOOw07Dd4L3gtqCmoK4AjgCD0HPQeCBYIFuQO5A+0B7QEjACMAYP5g/q/8r/wN+w37efl5+f/3//eY9pj2QfVB9Qf0B/To8ujy3vHe8fnw+fAv8C/weO947+Du4O5p7mnuFO4U7vnt+e0b7hvudO507g3vDe/h7+Hv6vDq8C7yLvKv86/zYvVi9UL3QvdE+UT5WPtY+2/9b/2C/4L/hQGFAWcDZwMiBSIFswazBhAIEAg9CT0JQQpBChELEQutC60LGgwaDE0MTQxPDE8MLwwvDO4L7guZC5kLPAs8C9QK1AppCmkKAwoDCqAJoAlFCUUJ9Qj1CKcIpwhaCFoICggKCK4HrgdBB0EHxQbFBjYGNgaZBZkF+AT4BFUEVQS0A7QDFQMVA3UCdQLPAc8BJgEmAX0AfQDW/9b/Nv82/57+nv4L/gv+eP14/ej86Pxa/Fr80vvS+1f7V/vl+uX6c/pz+v75/vl5+Xn52/jb+Cv4K/hp92n3l/aX9sn1yfUC9QL1RPRE9J3znfMO8w7zkvKS8jjyOPIB8gHy6fHp8QHyAfJI8kjytfK18k3zTfMI9Aj03fTd9M71zvXd9t32A/gD+ED5QPmN+o362PvY+xf9F/1B/kH+Uf9R/0kASQAwATABCQIJAtIC0gKGA4YDIQQhBJkEmQTxBPEELQUtBVAFUAVeBV4FVAVUBSkFKQXTBNMETwRPBKEDoQPXAtcCBwIHAkQBRAGbAJsADQANAJP/k/8h/yH/qf6p/iv+K/6m/ab9Iv0i/a38rfxL/Ev8+/v7+7j7uPt8+3z7QPtA+w77Dvvt+u365Prk+vb69voS+xL7Hfsd+wf7B/vF+sX6X/pf+vL58vmW+Zb5VPlU+S75LvkQ+RD55fjl+Kn4qfhi+GL4HPgc+O737vfb99v32vfa9+b35vfv9+/37fft9+v36/fw9/D3BPgE+DP4M/h6+Hr4zfjN+Cj5KPmA+YD50fnR+SX6JfqH+of6AfsB+5r7mvtN/E38Df0N/c39zf2I/oj+QP9A//v/+//AAMAAkAGQAV8CXwIgAyADygPKA1MEUwS9BL0EEAUQBUsFSwVxBXEFhwWHBYgFiAVzBXMFSgVKBQcFBwWqBKoEOQQ5BLcDtwMuAy4DpwKnAiQCJAKgAaABGAEYAYgAiAD1//X/Zf9l/+X+5f55/nn+IP4g/tb91v2U/ZT9UP1Q/Qz9DP3G/Mb8g/yD/E78Tvwp/Cn8FfwV/BH8EfwV/BX8F/wX/Bn8Gfwg/CD8Mvwy/Fb8VvyL/Iv8wvzC/PD88PwK/Qr9D/0P/Q39Df0Q/RD9JP0k/Uz9TP2D/YP9wf3B/QH+Af5D/kP+iv6K/tz+3P5A/0D/uf+5/0YARgDoAOgAmwGbAVkCWQIjAyMD/QP9A+UE5QThBeEF8QbxBgMIAwgLCQsJAwoDCtsK2wqbC5sLUQxRDPkM+QyYDZgNMA4wDq4Org4TDxMPZA9kD5gPmA+yD7IPuQ+5D54Png9kD2QPFg8WD64Org41DjUOsw2zDR0NHQ13DHcMyAvICwwLDAtHCkcKfQl9CaMIowi2B7YHtga2BqcFpwWQBJAEfgN+A3YCdgJ3AXcBgACAAI7/jv+g/qD+uP24/d783vwS/BL8VPtU+6j6qPoI+gj6bflt+dz43PhO+E74wvfC90P3Q/fU9tT2c/Zz9in2Kfbv9e/1vfW99Zn1mfWB9YH1dPV09YD1gPWm9ab13vXe9S32LfaJ9on26/br9ln3WffR99H3UfhR+N343fhy+XL5B/oH+p76nvo2+zb7z/vP+3D8cPwf/R/93P3c/aT+pP5u/27/MAAwAOMA4wCHAYcBIAIgAq8CrwIyAzIDogOiA/AD8AMRBBEEAQQBBL4DvgNHA0cDnQKdAr0BvQGgAKAARf9F/7n9uf39+/37F/oX+hX4Ffjx9fH1qfOp81fxV/EF7wXvxOzE7Lvqu+r06PTocOdw507mTuaL5YvlIOUg5RzlHOVs5Wzl9OX05bbmtuan56fnu+i76ATqBOqE64TrMO0w7Q3vDe8d8R3xWvNa88f1x/Vh+GH4EvsS+8D9wP1eAF4A5QLlAlMFUwW/B78HOgo6CrUMtQwxDzEPqhGqEQAUABQ5FjkWWhhaGEUaRRr0G/QbYh1iHWseax4THxMfcB9wH3cfdx8/Hz8f4x7jHlYeVh6pHakd8hzyHB8cHxw4GzgbShpKGkIZQhksGCwYIBcgFxcWFxYiFSIVTRRNFIcThxPPEs8SKBIoEoURhRHkEOQQRRBFEJIPkg/BDsEOzQ3NDa0MrQxjC2ML/An8CX4IfgjuBu4GVQVVBbcDtwMSAhICZwBnALn+uf4F/QX9UPtQ+6r5qvkW+Bb4lvaW9jP1M/Xn8+fzqfKp8o3xjfGS8JLwuu+67xTvFO+W7pbuMe4x7vDt8O3P7c/ty+3L7fjt+O1Z7lnu5O7k7qPvo++R8JHwp/Gn8eny6fJY9Fj06PXo9Zj3mPdj+WP5QftB+yr9Kv0V/xX/9QD1ALgCuAJZBFkE2gXaBTYHNgd1CHUImwmbCZwKnAp4C3gLMgwyDMMMwww0DTQNjQ2NDcQNxA3WDdYNyw3LDZsNmw1TDVMNAA0ADaEMoQw3DDcMyQvJC08LTwvQCtAKVgpWCtoJ2glVCVUJwAjACA4IDgg9Bz0HWQZZBm4FbgWHBIcErAOsA9sC2wIPAg8CRQFFAX4AfgC7/7v/+/77/j/+P/5//X/9svyy/Nz73Pv4+vj6CfoJ+h/5H/k8+Dz4ZPdk96P2o/bz9fP1SfVJ9ab0pvQA9AD0T/NP86jyqPIQ8hDyj/GP8TnxOfEO8Q7xCfEJ8TLxMvGH8YfxAPIA8qfyp/J383fzaPRo9Hj1ePWj9qP23ffd9yT5JPl2+nb60fvR+zj9OP2u/q7+MgAyALYBtgEvAy8DjQSNBMAFwAXKBsoGsgeyB3QIdAgTCRMJkQmRCeIJ4gkICggKDwoPCvcJ9wnECcQJdAl0CfkI+QhHCEcIYQdhB00GTQYQBRAFtAO0A0ACQAK6ALoANP80/8z9zP2V/JX8mvua+9f61/oy+jL6kfmR+fH48fhW+Fb4zPfM92T3ZPcZ9xn32/bb9qj2qPZ+9n72Y/Zj9mv2a/aT9pP2yvbK9v/2//Yb9xv3EfcR9+z27Paz9rP2bvZu9i72Lvb39ff1yvXK9bT1tPWu9a71rPWs9az1rPWm9ab1mPWY9Zn1mfWu9a711PXU9Qf2B/Y69jr2ZPZk9pX2lfbg9uD2TvdO9+b35veb+Jv4V/lX+Rb6Fvrg+uD6wvvC+8f8x/z0/fT9Pf89/5AAkADlAeUBPAM8A44EjgTiBeIFMwczB3EIcQiWCZYJnwqfCnwLfAssDCwMrgyuDPIM8gz6DPoM0gzSDHkMeQz4C/gLWAtYC5EKkQqmCaYJnwifCIIHggdYBlgGKQUpBfUD9QO6AroCegF6AT0APQAH/wf/3f3d/cb8xvy++777xvrG+uf55/ko+Sj5iviK+BL4Evi497j3cfdx9z73Pvcg9yD3GPcY9zD3MPdm92b3sPew9w/4D/iA+ID4AvkC+Zv5m/lL+kv6C/sL+9H70fuQ/JD8O/07/c39zf1F/kX+qf6p/gH/Af9X/1f/sf+x/w8ADwBtAG0AyQDJAB4BHgFzAXMB0QHRAToCOgKzArMCOwM7A8cDxwNZBFkE9QT1BJ8FnwViBmIGRwdHBz0IPQg/CT8JQQpBCi4LLgsGDAYM0AzQDIQNhA0qDioOyw7LDlgPWA/SD9IPPRA9EIsQixDBEMEQ5xDnEPMQ8xDkEOQQvxC/EHAQcBD1D/UPWA9YD5UOlQ62DbYNywzLDNIL0gvMCswKvAm8CZkImQhiB2IHGgYaBsEEwQRZA1kD6gHqAX0AfQAX/xf/vv2+/Xr8evxK+0r7LPos+ir5KvlC+EL4dPd098f2x/Y09jT2rvWu9Tj1OPXO9M70avRq9Bz0HPTi8+LztfO185zznPOQ85DzhfOF84bzhvON843zmfOZ87Pzs/Pc89zzEfQR9Fz0XPS69Lr0JfUl9aL1ovUv9i/2yfbJ9nf3d/c4+Dj4BPkE+dX51fmj+qP6aPto+yj8KPzp/On8sP2w/X/+f/5Y/1j/NgA2ABYBFgH5AfkB4ALgAsYDxgOsBKwEjQWNBVwGXAYWBxYHtwe3BzAIMAh9CH0ImwibCIIIgggwCDAIqAeoB+QG5AbgBeAFnQSdBB8DHwNmAWYBfP98/2z9bP0z+zP7y/jL+ET2RPaf85/z6/Dr8FXuVe7u6+7rxenF6fvn++eN5o3mceVx5bnkueRZ5FnkPOQ85Gvka+TU5NTkZeVl5S7mLuYx5zHna+hr6O7p7um+677r0O3Q7SjwKPC/8r/ygPWA9Vj4WPg1+zX7BP4E/rYAtgBYA1gD9QX1BY4IjggyCzIL4g3iDYIQghAOEw4TgBWAFbYXtherGasZXhteG6wcrByYHZgdMR4xHmkeaR5YHlgeHx4fHrcdtx03HTcduBy4HCgcKByOG44b7xrvGi8aLxpPGU8ZXxhfGFgXWBdRFlEWYxVjFYQUhBS6E7oTBRMFE1MSUxKgEaAR8xDzEDoQOhBsD2wPhg6GDnMNcw0vDC8MxQrFCj4JPgmqB6oHGQYZBpMEkwQUAxQDmAGYARwAHACW/pb+A/0D/W/7b/vY+dj5Q/hD+MD2wPZM9Uz15PPk85jymPJm8WbxTfBN8F7vXu+V7pXu6O3o7WXtZe0G7Qbtx+zH7Lvsu+zk7OTsPu0+7dPt0+2h7qHun++f79bw1vBD8kPy3PPc85r1mvV293b3Yflh+VP7U/tJ/Un9O/87/xsBGwHjAuMCjASMBA0GDQZqB2oHqwirCMgJyAnHCscKrguuC3MMcwwcDRwNsQ2xDSUOJQ54DngOrw6vDrwOvA6pDqkOhQ6FDk8OTw4MDgwOww3DDWcNZw36DPoMhwyHDAkMCQyDC4ML9Ar0CksKSwqACYAJmQiZCJwHnAeaBpoGpAWkBb0EvQTmA+YDGgMaA1gCWAKeAZ4B7QDtAEMAQwCX/5f/3v7e/hT+FP40/TT9QvxC/E37TftZ+ln6Z/ln+YP4g/ip96n31fbV9g/2D/ZO9U71i/SL9NDz0PMf8x/zffJ98gLyAvKz8bPxj/GP8Z/xn/Hh8eHxTPJM8uny6fKz87PznPSc9Jz1nPWp9qn2tve298f4x/ji+eL5CPsI+zv8O/x3/Xf9s/6z/uj/6P8VARUBOQI5AkoDSgNCBEIEGAUYBcIFwgVDBkMGpAakBuwG7AYjByMHTgdOB2IHYgdZB1kHMAcwB+EG4QZqBmoGywXLBQIFAgUHBAcE3ALcAosBiwEfAB8Ar/6v/lf9V/0o/Cj8LPss+2n6afrP+c/5SvlK+dD40PhT+FP4zvfO91H3Uffk9uT2i/aL9lH2UfYv9i/2GvYa9hX2FfYa9hr2IvYi9jT2NPZB9kH2NvY29gz2DPa59bn1QPVA9cD0wPRT9FP0DPQM9P/z//Mi9CL0XfRd9Kf0p/Tx9PH0NvU29Yb1hvXn9ef1WfZZ9t723vZv92/3BPgE+Kb4pvhd+V35L/ov+iP7I/sy/DL8Uf1R/XH+cf6O/47/pgCmALoBugHUAtQC9gP2AxsFGwVABkAGYgdiB3MIcwh0CXQJZApkCjcLNwvrC+sLfgx+DOAM4AwODQ4NDQ0NDdUM1QxuDG4M5AvkCzULNQtkCmQKeQl5CXIIcghXB1cHMgYyBgkFCQXfA98DtgK2Ao4BjgFkAGQAP/8//yv+K/4v/S/9T/xP/I/7j/vn+uf6UfpR+tL50vlt+W35Jvkm+Qb5BvkI+Qj5Ifkh+Uz5TPmD+YP5wvnC+RL6Evp4+nj68frx+n37ffsT/BP8qvyq/D/9P/3P/c/9Wv5a/tr+2v5L/0v/pf+l/+T/5P8KAAoAHQAdACcAJwAwADAAQgBCAGAAYACLAIsAxADEAAYBBgFRAVEBpAGkAfsB+wFZAlkCvgK+AisDKwOlA6UDNAQ0BNME0wSIBYgFUQZRBiEHIQf3B/cHzgjOCJUJlQlMCkwK9Ar0CoALgAv4C/gLYgxiDLcMtwz9DP0MPg0+DW8Nbw2TDZMNqg2qDaQNpA19DX0NNg02DcMMwwwqDCoMdQt1C6UKpQq+Cb4JygjKCMYHxgezBrMGmQWZBXkEeQRUA1QDKwIrAv8A/wDL/8v/kP6Q/lj9WP0o/Cj8CvsK+wz6DPov+S/5bfht+Mn3yfc49zj3sfax9jv2O/bQ9dD1a/Vr9RT1FPXD9MP0bvRu9B70HvTQ89DzhfOF81DzUPMy8zLzKPMo8zbzNvNS81Lzb/Nv85fzl/PN883zFvQW9ID0gPQO9Q71uPW49X32ffZZ91n3RvhG+Ef5R/ld+l36gPuA+6f8p/zJ/cn93f7d/t7/3v/QANAAuwG7AaACoAKIA4gDeQR5BGoFagVeBl4GUAdQBzIIMgj/CP8Itgm2CUoKSgrACsAKGQsZC0sLSwtPC08LHgseC6oKqgrtCe0J6QjpCKEHoQcUBhQGRAREBDYCNgLi/+L/Sv1K/YL6gvqM94z3c/Rz9FrxWvFJ7knuTOtM643ojegT5hPm4+Pj4x/iH+LD4MPgxd/F3z3fPd8g3yDfVd9V3+bf5t+84Lzgu+G74fLi8uJf5F/k/uX+5eTn5OcR6hHqc+xz7AXvBe+98b3xi/SL9Gr3avdb+lv6Vv1W/UwATABBA0EDMgYyBhUJFQn2C/YL2w7bDqwRrBFpFGkUDBcMF28ZbxmQG5Abbh1uHege6B7/H/8fwCDAIBghGCEfIR8h9iD2IJsgmyAmICYgsB+wHyYfJh+MHowe8B3wHTodOh1zHHMcrhuuG9oa2hoGGgYaQhlCGXsYexi3F7cX/Rb9FjkWORZnFWcVjBSMFJMTkxN0EnQSLhEuEbQPtA8GDgYOMgwyDEAKQAo8CDwIOQY5BkEEQQRSAlICbABsAJH+kf64/Lj84frh+hj5GPlb91v3rPWs9Rf0F/SY8pjyJfEl8dHv0e+b7pvugu2C7aDsoOzx6/Hraetp6xLrEuvi6uLq0+rT6vrq+upd613r+ev569zs3OwB7gHuWe9Z7+jw6PCq8qryl/SX9Kr2qvbb+Nv4GfsZ+1X9Vf2G/4b/ogGiAZ4DngN5BXkFNAc0B8gIyAg5CjkKjQuNC7cMtwy6DboNmw6bDk8PTw/gD+APXRBdELoQuhD/EP8QMhEyEUMRQxE4ETgRHREdEegQ6BCfEJ8QSxBLENsP2w9SD1IPtw63DgQOBA46DToNYAxgDG0LbQtjCmMKSglKCScIJwgGBwYH8AXwBeYE5gTkA+QD7QLtAgACAAIgASABTQBNAIX/hf+9/r3+6v3q/Qn9Cf0X/Bf8Gvsa+x76Hvol+SX5LPgs+Dr3OvdH9kf2UvVS9Wj0aPSI84jzrfKt8uXx5fEv8S/xifCJ8ArwCvC277bvjO+M75nvme/Z79nvQPBA8NPw0/CK8YrxWfJZ8kPzQ/NB9EH0SvVK9WP2Y/aJ94n3ufi5+PT59Pk5+zn7gvyC/Mv9y/0V/xX/WQBZAJABkAG3ArcCyAPIA7sEuwSVBZUFWgZaBgYHBgedB50HHwgfCIMIgwjDCMMI3wjfCM4IzgiNCI0IIAggCIEHgQevBq8GrwWvBYUEhQQ5AzkD2QHZAXkAeQAn/yf/9P30/e387fwO/A78S/tL+5v6m/rs+ez5Mfkx+W74bvim96b33Pbc9iL2IvZ79Xv14vTi9GP0Y/T38/fzlfOV80bzRvMC8wLzvPK88nfyd/Iq8iryzPHM8W7xbvEd8R3x5vDm8Ojw6PAs8SzxqvGq8VzyXPIq8yrz+PP488X0xfSO9Y71WPZY9i/3L/cV+BX4BfkF+fr5+vn3+vf6/fv9+xX9Ff1I/kj+lP+U/+oA6gA/Aj8CiAOIA7sEuwTfBd8F/gb+BhYIFgguCS4JRQpFCkkLSQs0DDQMAw0DDaINog0SDhIOWQ5ZDmwObA5QDlAODA4MDpYNlg3xDPEMJAwkDDMLMwsoCigKFQkVCf0H/QfkBuQGyAXIBaYEpgR8A3wDTQJNAiMBIwH/////5/7n/t/93/3n/Of8//v/+y/7L/ty+nL6xvnG+Sz5LPmh+KH4Jvgm+Mb3xveG94b3Yfdh91r3Wvdo92j3gveC96r3qvfj9+P3Kvgq+IX4hfju+O74Wvla+cb5xvkt+i36jPqM+ub65vo9+z37jPuM+9b71vsW/Bb8S/xL/Hf8d/yf/J/8yPzI/P78/vxK/Ur9r/2v/Sz+LP66/rr+UP9Q/+j/6P+DAIMAKQEpAdwB3AGiAqICeQN5A1YEVgQ4BTgFHwYfBgUHBQfxB/EH5QjlCNAJ0AmvCq8Kfwt/CzEMMQzNDM0MXQ1dDdcN1w1CDkIOpQ6lDvMO8w4sDywPVw9XD2QPZA9QD1APIA8gD8YOxg5HDkcOrA2sDe8M7wwVDBUMJwsnCyMKIwoQCRAJ+Qf5B+IG4gbKBcoFtAS0BJsDmwN6AnoCVQFVATEAMQAS/xL//f39/fz8/PwP/A/8Nvs2+3f6d/rL+cv5Kvkq+Zb4lvgG+Ab4c/dz9+X25fZa9lr20PXQ9VP1U/Xg9OD0dvR29CP0I/Tl8+Xzt/O386DzoPOY85jzl/OX86Pzo/O9873z5fPl8yv0K/SR9JH0FPUU9bn1ufV+9n72W/db91D4UPhX+Vf5Zfpl+nP7c/t//H/8hP2E/YX+hf6J/4n/lACUAKYBpgG9Ar0C1QPVA+IE4gTkBeQF2QbZBrwHvAeQCJAIVwlXCQEKAQqKCooK7gruCiELIQskCyQL/gr+CqcKpwoaChoKTQlNCTMIMwi8BrwG7ATsBNQC1AKBAIEABf4F/nn7efvU+NT4EPYQ9jjzOPNJ8EnwTO1M7XbqdurZ59nng+WD5Zfjl+MO4g7i1uDW4APgA+CQ35Dfcd9x38DfwN9u4G7gXeFd4YriiuLd493jQeVB5crmyuaJ6Inoh+qH6tns2ex+737vXPJc8lz1XPVo+Gj4bvtu+2P+Y/5TAVMBRgRGBDIHMgccChwK/wz/DMEPwQ9lEmUS9BT0FFcXVxeUGZQZqhuqG2sdax3JHskewx/DH0AgQCBZIFkgOyA7IOwf7B+NH40fOR85H9ge2B5vHm8eBB4EHn8dfx3nHOccThxOHJ4bnhveGt4aGhoaGkEZQRldGF0YgBeAF58WnxbBFcEV6BToFPwT/BPvEu8SvBG8EVIQUhCwDrAO5AzkDPUK9QrvCO8I5gbmBuQE5ATtAu0CBgEGATX/Nf9u/W79rPus+/H58fk0+DT4cfZx9rn0ufQJ8wnzZvFm8eXv5e+H7ofuS+1L7UPsQ+xs62zrueq56j7qPur06fTp0+nT6e/p7+lI6kjq1urW6qnrqeu/7L/sEO4Q7qPvo+9z8XPxcvNy85b1lvXV99X3HPoc+l78XvyY/pj+xADEANcC1wLTBNMEtwa3BnQIdAgPCg8KjAuMC98M3wwNDg0OHw8fDwgQCBDPEM8QfhF+EQYSBhJtEm0SuxK7EuES4RLoEugS2hLaEq0SrRJlEmUSCBIIEooRihHvEO8QQxBDEIIPgg+0DrQO4A3gDf8M/wwLDAsMCgsKC/cJ9wnXCNcItwe3B5sGmwaHBYcFfwR/BIIDggOMAowCnQGdAbEAsQDC/8L/yv7K/sf9x/2y/LL8iPuI+1P6U/oU+RT50PfQ95n2mfZx9XH1V/RX9FjzWPNs8mzyivGK8b/wv/AI8AjwZO9k7+fu5+6W7pbube5t7nzufO7C7sLuNe81793v3e+y8LLwp/Gn8bzyvPLq8+rzJfUl9W/2b/bG98b3J/kn+ZL6kvoJ/An8iv2K/RL/Ev+hAKEALwIvAq4DrgMWBRYFYAZgBn0HfQduCG4IOAk4CdUJ1QlLCksKngqeCsYKxgrECsQKnAqcCkcKRwrECcQJFgkWCTkIOQgpBykH6gXqBX8EfwTtAu0CRQFFAZr/mv/+/f79hPyE/Dz7PPsn+if6O/k7+XP4c/i697r3AfcB90v2S/aV9ZX14PTg9D30PfSu867zMfMx89Ty1PKT8pPyZfJl8lPyU/JO8k7yRvJG8kDyQPIu8i7yC/IL8ubx5vHD8cPxqfGp8a/xr/Hf8d/xOvI68sfyx/J483jzNPQ09O/07/Sb9Zv1MPYw9r32vfZO90736ffp95j4mPhb+Vv5L/ov+hj7GPsc/Bz8O/07/XH+cf66/7r/CwELAVUCVQKUA5QDywTLBPIF8gUSBxIHLAgsCDcJNwkzCjMKHgseC+YL5guFDIUM+Qz5DDANMA0sDSwN+Az4DJMMkwwEDAQMVgtWC4MKgwqQCZAJiAiICHEHcQdRBlEGMgUyBRIEEgTpAukCtgG2AXwAfAA+/z7/A/4D/tn82fzB+8H7vPq8+tL50vkC+QL5SfhJ+K/3r/cx9zH3yvbK9oL2gvZZ9ln2TfZN9mb2Zvag9qD29Pb09mH3Yffk9+T3dvh2+Bj5GPnH+cf5evp6+iz7LPvY+9j7ePx4/Av9C/2T/ZP9DP4M/nX+df7N/s3+E/8T/0n/Sf90/3T/m/+b/8T/xP/4//j/OgA6AI0AjQDxAPEAZAFkAd8B3wFgAmAC5gLmAm8DbwMABAAEnAScBD4FPgXpBekFngaeBlYHVgcUCBQI3gjeCKgJqAl0CnQKQAtAC/wL/AupDKkMSg1KDdMN0w1IDkgOsg6yDgcPBw9MD0wPhQ+FD6IPog+gD6APfw9/DzIPMg+6DroOJg4mDnENcQ2kDKQMxQvFC88KzwrCCcIJqQipCIUHhQdgBmAGQQVBBSkEKQQSAxID+QH5AdsA2wC3/7f/k/6T/nv9e/10/HT8g/uD+6z6rPro+ej5Lfkt+X34ffjT99P3MPcw96H2ofYn9if2u/W79WL1YvUQ9RD1vvS+9Hj0ePRE9ET0IfQh9B30HfQs9Cz0P/Q/9FT0VPRo9Gj0evR69J70nvTd9N30OPU49bX1tfVN9k32+Pb49rf3t/eK+Ir4bvlu+WX6Zfpv+2/7ivyK/LH9sf3n/uf+JwAnAGkBaQGtAq0C8gPyAy8FLwVnBmcGmQeZB7UItQi3CbcJnAqcClcLVwvwC/ALbQxtDMgMyAz/DP8MDg0ODeMM4wxyDHIMtwu3C6kKqQpFCUUJkweTB5sFmwVgA2AD6QDpAET+RP5n+2f7U/hT+Bz1HPXD8cPxVu5W7gPrA+vT59PnyOTI5AriCuKX35ffbd1t3bvbu9uI2ojazdnN2aDZoNnm2ebZd9p32lXbVdty3HLcu9273U7fTt8u4S7hTuNO47nlueVo6GjoSetJ61vuW+6b8Zvx+vT69Gn4afjh++H7VP9U/7ICsgIEBgQGTglOCYMMgwyuD64P1BLUEtgV2BW6GLoYdht2G+Ad4B3wH/AfqSGpIegi6CK/I78jSiRKJH4kfiR4JHgkWiRaJBUkFSS6I7ojXyNfI+ki6SJeIl4iyiHKIREhESE+ID4gYx9jH3Mecx54HXgdhRyFHIUbhRt6GnoacBlwGVEYURgbFxsXzRXNFVEUURScEpwSshCyEJMOkw5LDEsM9An0CZwHnAdLBUsFBwMHA9MA0wCn/qf+gfyB/G36bfpk+GT4ZfZl9nv0e/Sf8p/yy/DL8BPvE+947Xjt++v767fqt+qt6a3p1ejV6D7oPujd593noeeh553nnefP58/nN+g36Oro6ujq6erpKesp66rsquxk7mTuSfBJ8FryWvKW9Jb07/bv9lr5WvnM+8z7Nv42/okAiQDGAsYC7wTvBPcG9wbjCOMIsgqyClcMVwzXDdcNOg86D3QQdBCNEY0RjhKOEmUTZRMaFBoUuBS4FC4VLhWEFYQVwxXDFdYV1hXDFcMVlRWVFUIVQhXUFNQUVxRXFL8TvxMPEw8TTRJNEmwRbBFwEHAQXw9fDzQONA7wDPAMnQudCzwKPArTCNMIawdrBwcGBwalBKUERgNGA+oB6gGOAI4ALv8u/8/9z/1m/Gb87Prs+mj5aPnZ99n3RPZE9sL0wvRW81bz//H/8cnwyfCp76nvk+6T7pztnO3C7MLsB+wH7H/rf+so6yjr9ur26vrq+uo06zTrnuue60rsSuwz7TPtSe5J7onvie/l8OXwTPJM8r3zvfM59Tn1uva69kX4Rfja+dr5c/tz+wv9C/2f/p/+JwAnAJsBmwH5AvkCPwQ/BGIFYgVjBmMGQwdDB/gH+AeHCIcI9wj3CEMJQwlyCXIJhgmGCXgJeAlDCUMJ6QjpCGMIYwixB7EH1wbXBtcF1wWzBLMEcQNxAxsCGwK3ALcAU/9T//79/v3B/MH8pful+7P6s/ro+ej5Pvk++bb4tvhC+EL41/fX93f3d/cW9xb3rfat9kP2Q/bU9dT1X/Vf9fT09PST9JP0N/Q39Obz5vOQ85DzJPMk86vyq/Im8ibymfGZ8SHxIfHE8MTwgfCB8GbwZvBx8HHwoPCg8AHxAfGT8ZPxR/JH8hbzFvPu8+7zvPS89In1ifVd9l32Q/dD90n4Sfhy+XL5tfq1+gr8Cvxs/Wz92P7Y/koASgDGAcYBRANEA7cEtwQYBhgGZwdnB5oImgi4CbgJyQrJCr4LvguYDJgMUw1TDdsN2w0tDi0OSw5LDisOKw7PDc8NQw1DDYQMhAyaC5oLkgqSCmwJbAkvCC8I5gbmBpgFmAVLBEsEBwMHA88BzwGiAKIAff99/2b+Zv5c/Vz9Zvxm/Iz7jPvP+s/6Lfot+qn5qflA+UD57Pjs+Lb4tvia+Jr4k/iT+KL4ovjB+MH47Pjs+Cf5J/l2+Xb52PnY+Uz6TPrO+s76UvtS+9T71PtP/E/8wfzB/C/9L/2V/ZX98P3w/Tv+O/5w/nD+jv6O/pv+m/6f/p/+ov6i/qz+rP7A/sD+3f7d/gT/BP88/zz/jP+M//j/+P+FAIUAMQExAe8B7wG5ArkCjAOMA2IEYgRBBUEFMAYwBigHKAcsCCwIOwk7CUYKRgpMC0wLUAxQDEINQg0gDiAO7A7sDpAPkA8QEBAQcxBzEK4QrhDMEMwQ1xDXEMYQxhCaEJoQWBBYEPAP8A9iD2IPsQ6xDtkN2Q3dDN0MyQvJC54KngpgCWAJFwgXCMQGxAZtBW0FGwQbBNYC1gKdAZ0BcQBxAE//T/8v/i/+D/0P/fb79vvs+uz69vn2+SP5I/lw+HD41/fX91f3V/fm9ub2e/Z79h72HvbL9cv1ffV99Tr1OvX89Pz0uvS69ID0gPRO9E70JfQl9BT0FPQY9Bj0J/Qn9ET0RPRi9GL0ePR49JD0kPSs9Kz00PTQ9Aj1CPVb9Vv1yfXJ9Vr2WvYR9xH36ffp9+D44Pjw+fD5C/sL+yv8K/xT/VP9g/6D/sD/wP8QARABcwJzAtoD2gNBBUEFogaiBu8H7wcsCSwJWgpaCmwLbAtfDF8MMA0wDc0NzQ03DjcOdQ51DoAOgA5bDlsOBQ4FDmsNaw18DHwMLwsvC3wJfAliB2IH8wTzBEYCRgJj/2P/VfxV/C35Lfng9eD1bfJt8vPu8+5163Xr/+f/58Pkw+TG4cbhC98L37vcu9zW2tbaU9lT2V3YXdjt1+3X79fv13XYddhk2WTZldqV2gvcC9y53bndi9+L35rhmuHu4+7jgeaB5l7pXumF7IXs4O/g72XzZfMI9wj3t/q3+l3+Xf74AfgBgQWBBekI6Qg7DDsMfQ99D5oSmhKbFZsVhRiFGDUbNRuuHa4d8h/yH9kh2SFiI2IjlSSVJFIlUiWtJa0lySXJJZ8lnyVUJVQlCyULJa8kryRKJEok5iPmI2IjYiPBIsEiFSIVIkchRyFjIGMgeh96H3cedx5gHWAdRRxFHBUbFRvWGdYZkhiSGDYXNhe4FbgVGBQYFEISQhI0EDQQ+g36DZwLnAsnCScJsQaxBkYERgTnAecBl/+X/1z9XP0q+yr7//j/+Ob25vbW9Nb0zfLN8tzw3PAB7wHvOe057aLrous96j3qBekF6RPoE+hf51/n3ebd5p/mn+af5p/m0ubS5knnSecB6AHo7uju6BrqGuqE64TrIe0h7fju+O4B8QHxLfMt83D1cPXD98P3F/oX+mb8Zvyx/rH+8wDzAB8DHwM0BTQFLgcuB/8I/wiwCrAKSQxJDL8Nvw0bDxsPYxBjEIQRhBGEEoQSahNqEyUUJRS8FLwUOBU4FYkViRW1FbUVxRXFFakVqRVlFWUVBBUEFXoUehTSE9ITHhMeE1YSVhJ9EX0RmBCYEJgPmA98DnwOTg1ODQwMDAy9Cr0KbAlsCRcIFwi7BrsGWwVbBfoD+gOZApkCPQE9Aen/6f+Q/pD+LP0s/bv7u/s1+jX6m/ib+AD3APdm9Wb10/PT813yXfIE8QTxye/J777uvu7d7d3tHe0d7Yrsiuwc7BzsyuvK66frp+u267br8+vz627sbuwk7STtB+4H7hnvGe9S8FLwo/Gj8QvzC/OE9IT0AfYB9oD3gPf8+Pz4cPpw+tz73PtE/UT9q/6r/g4ADgBtAW0BwwLDAgQEBAQqBSoFMQYxBgwHDAe9B70HSQhJCKsIqwjrCOsIEQkRCRoJGgkHCQcJ3AjcCI8IjwgeCB4IigeKB88GzwbrBesF4gTiBLYDtgNsAmwCEgESAbr/uv93/nf+W/1b/XX8dfzG+8b7RPtE++T65PqQ+pD6Nfo1+s35zflS+VL5wvjC+Cz4LPiT95P3+Pb49mb2ZvbX9df1QfVB9a/0r/QZ9Bn0ePN489fy1/Ix8jHyfvF+8dDw0PAr8CvwmO+Y7zrvOu8a7xrvOu86757vnu8y8DLw2PDY8IjxiPE18jXy3PLc8ovzi/NN9E30IvUi9RH2EfYZ9xn3Nfg1+Gn5afm6+rr6Kvwq/LT9tP1Q/1D/8gDyAIgCiAIOBA4EhwWHBesG6wZDCEMIjwmPCcEKwQrSC9ILwQzBDHsNew0BDgEOVg5WDm8Obw5LDksO8A3wDVgNWA2ODI4MoQuhC5gKmAp8CXwJWghaCDIHMgcIBggG4ATgBL8DvwOlAqUClgGWAZMAkwCZ/5n/pf6l/r/9v/3n/Of8JPwk/H77fvvz+vP6fvp++hz6HPrE+cT5c/lz+TD5MPkB+QH56Pjo+On46fgB+QH5Jvkm+Vf5V/mQ+ZD5z/nP+Rb6Fvpj+mP6rvqu+vX69fox+zH7X/tf+4L7gvua+5r7q/ur+7r7uvvJ+8n72fvZ+/D78PsR/BH8P/w//IP8g/zm/Ob8bf1t/Rr+Gv7p/un+0f/R/8QAxAC+Ab4BvAK8Ar0DvQPKBMoE5QXlBQQHBAcjCCMIQQlBCU8KTwpTC1MLVAxUDEYNRg0pDikO/A78DqoPqg8yEDIQnBCcEOAQ4BAHEQcRHxEfERwRHBH9EP0QxxDHEGgQaBDfD98PNg82D2cOZw56DXoNfAx8DGwLbAtLCksKIQkhCesH6weuBq4GdQV1BUYERgQnAycDGQIZAhsBGwEkACQAL/8v/z7+Pv5R/VH9bPxs/Jj7mPvW+tb6JPok+oj5iPn7+Pv4d/h3+P33/feD94P3APcA9332ffb39ff1bfVt9ez07PRx9HH09vP284XzhfMb8xvztfK18mTyZPIl8iXy7vHu8cfxx/Gp8anxkvGS8ZLxkvG08bTx/PH88XjyePIo8yjzBfQF9BH1EfVJ9kn2pfel9yD5IPm2+rb6XPxc/Az+DP7H/8f/iwGLAVIDUgMYBRgF1wbXBnoIegj9Cf0JYwtjC58Mnwy5DbkNtw63DokPiQ8vEC8QrRCtEPIQ8hD8EPwQzhDOEFkQWRCWD5YPhA6EDhoNGg1OC04LIQkhCZQGlAanA6cDZgBmAPH88fxP+U/5jPWM9cPxw/Hs7eztBuoG6j7mPuao4qjiX99f36Hcodx12nXax9jH2KrXqtcF1wXXwNbA1v7W/ta517nX2tja2GjaaNpB3EHcN9433k/gT+CL4ovi6uTq5Irniud06nTqlu2W7eTw5PBM9Ez0t/e39xn7Gft8/nz+5AHkAUsFSwW8CLwINAw0DJIPkg/SEtIS8RXxFc4Yzhh1G3Ub7x3vHRkgGSDxIfEhdSN1I3wkfCQUJRQlYiViJWYlZiVKJUolNCU0JQ8lDyXgJOAksySzJGgkaCQNJA0kuSO5I1YjViPqIuoigSKBIvoh+iFZIVkhsCCwIOgf6B8HHwcfFR4VHvUc9RydG50bEBoQGjgYOBgYFhgWwRPBEzYRNhGFDoUOxQvFCwAJAAk9Bj0GhAOEA94A3gBJ/kn+w/vD+1b5Vvn19vX2lvSW9EjySPIG8Abw0+3T7dDr0Ov86fzpU+hT6Ojm6Oav5a/ln+Sf5NPj0+NM40zjA+MD4xHjEeNr42vjAOQA5ODk4OQN5g3mfud+50TpROla61rrp+2n7SDwIPC08rTyS/VL9d733vdr+mv66/zr/Ff/V/+vAa8B7QPtAwQGBAb6B/oH0gnSCYYLhgseDR4NpA6kDgcQBxBNEU0ReBJ4EncTdxNSFFIUGBUYFbwVvBVJFkkWyRbJFiQXJBdYF1gXbBdsF04XThcFFwUXohaiFhgWGBZtFW0VqhSqFMETwRO4ErgSnxGfEXQQdBA+Dz4PBw4HDskMyQx+C34LKgoqCsYIxghVB1UH2wXbBVsEWwTRAtECOgE6AZf/l//f/d/9EPwQ/Dn6Ofpa+Fr4efZ59qr0qvTr8uvyPPE88bDvsO9F7kXu+ez57OHr4ev66vrqPOo86rnpuelr6WvpSulK6WjpaOm/6b/pRupG6gXrBevz6/Pr/+z/7CzuLO5w73DvvvC+8BfyF/J683rz4PTg9FD2UPbM98z3TvlO+dP60/pW/Fb8zv3O/TT/NP+HAIcAxgHGAeoC6gL0A/QD4QThBKoFqgVQBlAG1wbXBjwHPAeAB4AHpwenB6kHqQeIB4gHQwdDB9gG2AZHBkcGlgWWBcgEyATjA+MD8QLxAvcB9wH/AP8AEQARADj/OP97/nv+4P3g/Wv9a/0S/RL9yfzJ/Ib8hvw5/Dn81/vX+2D7YPvR+tH6J/on+m/5b/mp+Kn42vfa9xD3EPdK9kr2gPWA9bv0u/Ty8/LzIfMh81vyW/Kh8aHx8fDx8FzwXPDg7+Dve+9770LvQu877zvvYO9g77Xvte8o8CjwnvCe8BrxGvGa8ZrxIvIi8sjyyPKS85LzfvR+9JD1kPXF9sX2HPgc+Jf5l/k3+zf78vzy/LT+tP51AHUAKgIqAskDyQNYBVgF3AbcBkYIRgiXCZcJygrKCtEL0QutDK0MYQ1hDd4N3g0hDiEOLQ4tDvgN+A2LDYsN9gz2DDwMPAxnC2cLhAqECpAJkAmRCJEIjgeOB4cGhwZ8BXwFdAR0BG8DbwNuAm4CdgF2AYkAiQCm/6b/zf7N/gT+BP5N/U39r/yv/DH8MfzQ+9D7g/uD+0b7RvsV+xX77frt+tn62frc+tz69Pr0+h77HvtS+1L7hfuF+7n7ufvu++77JPwk/Fv8W/yO/I78s/yz/Mn8yfzM/Mz8v/y//Kj8qPyL/Iv8bPxs/FH8Ufw//D/8Ofw5/EX8Rfxp/Gn8p/yn/AP9A/1//X/9G/4b/s/+z/6W/5b/agBqAEMBQwEmAiYCFQMVAw8EDwQXBRcFLAYsBkMHQwdeCF4IgQmBCaEKoQq/C78L2AzYDNcN1w25DrkOgA+ADyEQIRClEKUQExETEV8RXxGMEYwRnxGfEYsRixFUEVQRAxEDEYcQhxDjD+MPHg8eDzIOMg4qDSoNEwwTDO8K7wrCCcIJlQiVCGkHaQdEBkQGLQUtBSEEIQQaAxoDEQIRAgEBAQHo/+j/yv7K/rX9tf2q/Kr8rPus+8H6wfrj+eP5D/kP+Uz4TPiU95T34Pbg9jL2MvaE9YT10vTS9Cz0LPSY85jzFvMW87XytfJs8mzyMPIw8gbyBvLk8eTxv/G/8aHxofGG8YbxafFp8VzxXPFj8WPxffF98brxuvEf8h/yqPKo8mPzY/NR9FH0bPVs9bL2svYb+Bv4mfmZ+Sb7JvvD/MP8b/5v/iYAJgDlAeUBoQOhA0YFRgXOBs4GNwg3CHsJewmkCqQKuQu5C6sMqwx6DXoNJg4mDqMOow72DvYOJw8nDywPLA8ADwAPoQ6hDvwN/A0GDQYNuwu7CxIKEgoDCAMIlAWUBdEC0QK+/77/aPxo/Or46vhB9UH1dfF18antqe3l6eXpP+Y/5vHi8eIM4Azgld2V3bHbsdtS2lLaYdlh2fXY9dgA2QDZadlp2UDaQNpw23Db09zT3GzebN4t4C3gBeIF4gzkDORN5k3mxOjE6Hnreetm7mbucPFw8Yj0iPSp96n3zPrM+vD98P0nAScBeAR4BNQH1Ac9Cz0LrA6sDvoR+hEnFScVMBgwGPAa8BplHWUdkB+QH0chRyGSIpIihSOFIxQkFCRhJGEklySXJK0krSS4JLgkzCTMJMkkySS1JLUkoiSiJHMkcyQ2JDYkAiQCJLwjvCNvI28jKSMpI8siyyJWIlYi0iHSIRwhHCEtIC0gCh8KH5UdlR3PG88bxRnFGXQXdBfrFOsURxJHEpMPkw/bDNsMLgouCo8Hjwf5BPkEbQJtAuv/6/9p/Wn94/rj+mH4Yfje9d71XvNe8/3w/fC87rzunOyc7LPqs+r06PToU+dT5+bl5uWq5KrkmeOZ49Di0OJN4k3iA+ID4g3iDeJr4mviGOMY4ynkKeSb5ZvlWOdY51zpXOmT65Pr4e3h7UDwQPCk8qTy//T/9E/3T/eU+ZT5zvvO+/v9+/0eAB4ANAI0Ai4ELgQOBg4G2AfYB4UJhQkkCyQLuwy7DDkOOQ6fD58P8RDxEB8SHxI2EzYTRhRGFD4VPhUbFhsW2xbbFmAXYBesF6wXzxfPF78XvxeJF4kXPRc9F8kWyRYvFi8WfxV/FbAUsBTNE80T5BLkEukR6RHeEN4Qyg/KD6MOow5rDWsNJwwnDNEK0QphCWEJ2wfbBz0GPQaFBIUEtgK2AtUA1QDb/tv+zfzN/Lv6u/qp+Kn4ofah9rf0t/Tm8ubyJ/En8Yvvi+8R7hHuuOy47Jrrmuu16rXq/On86YDpgOk36TfpGekZ6TnpOemP6Y/pCeoJ6qrqqupj62PrJOwk7Pvs++zp7ent6+7r7gvwC/BF8UXxjPKM8uTz5PNJ9Un1tfa19ij4KPie+Z75DfsN+238bfy9/b399v72/hUAFQAbARsBBwIHAtYC1gKIA4gDHQQdBI0EjQTSBNIE6AToBMsEywR+BH4EDwQPBIoDigP7AvsCcQJxAvQB9AGNAY0BRAFEAR8BHwEbARsBMQExAVABUAFkAWQBXAFcAS4BLgHaANoAZwBnAOH/4f9S/1L/v/6//iz+LP6R/ZH96Pzo/DH8Mfxp+2n7j/qP+q35rfnG+Mb42ffZ9/b29vYi9iL2Y/Vj9cr0yvRZ9Fn0BfQF9M3zzfOd853zZPNk8ynzKfPt8u3yt/K38pzynPKi8qLyxvLG8hHzEfOD84PzFvQW9NL00vS29bb1uva69tv32/cT+RP5W/pb+q/7r/sR/RH9gP6A/vP/8/9nAWcB0wLTAigEKARfBV8FdgZ2BmAHYAchCCEIuwi7CCoJKglxCXEJmAmYCZoJmgl9CX0JSglKCf8I/wihCKEINwg3CLwHvAczBzMHngaeBvwF/AVQBVAFoASgBO0D7QM6AzoDjAKMAuUB5QFGAUYBsQCxACsAKwC0/7T/UP9Q/wH/Af/H/sf+ov6i/pD+kP6P/o/+nf6d/rj+uP7g/uD+D/8P/0L/Qv9z/3P/m/+b/7f/t//F/8X/xP/E/7T/tP+S/5L/Xf9d/xb/Fv+//r/+Xf5d/vf99/2R/ZH9Lf0t/dL80vyB/IH8PPw8/Aj8CPzp++n73Pvc++T75Pv++/77K/wr/G78bvzK/Mr8Qv1C/dX91f2C/oL+R/9H/x0AHQAFAQUB/QH9Af8C/wINBA0EJwUnBUQGRAZkB2QHhwiHCJ4JngmsCqwKswuzC6MMowyADYANSg5KDuwO7A5kD2QPtQ+1D9IP0g/DD8MPlQ+VD0EPQQ/MDswOOw47DoYNhg2yDLIMzAvMC9kK2QriCeIJ7wjvCPsH+wcFBwUHDwYPBhkFGQUmBCYEOwM7A1oCWgKAAYABqQCpANX/1f8A/wD/KP4o/lT9VP2D/IP8tvu2+/T69Po6+jr6hvmG+d743vg9+D34ofeh9xP3E/eR9pH2GPYY9rH1sfVV9VX1/fT99K/0r/Rk9GT0FvQW9NDz0POQ85DzU/NT8ybzJvMI8wjz8/Lz8vPy8/II8wjzMfMx83/zf/P48/jzmfSZ9Gf1Z/Va9lr2Zfdl94T4hPi5+bn5AvsC+2T8ZPzd/d39ZP9k/+gA6ABgAmACxQPFAxAFEAVIBkgGdQd1B44IjgiWCZYJiwqLCl8LXwsQDBAMogyiDAYNBg05DTkNNw03DfAM8AxVDFUMXwtfCwQKBAo+CD4IFQYVBp0DnQPfAN8A6v3q/dD60PqM94z3HfQd9KLwovAl7SXtuum66ZjmmObK48rjUuFS4VHfUd++3b7dityK3NPb09uN243boduh2x3cHdzp3Onc6N3o3SXfJd+X4JfgLuIu4v7j/uMJ5gnmR+hH6MLqwup67XrtW/Bb8F3zXfN39nf2nfmd+cn8yfwGAAYAVwNXA60GrQYGCgYKWA1YDYIQghCEE4QTYRZhFvoY+hhPG08bYx1jHRAfEB9fIF8gZCFkIRIiEiKGIoYi4CLgIhEjESMwIzAjVCNUI2YjZiNyI3IjiCOII4UjhSNwI3AjWiNaIysjKyPyIvIiwiLCIn0ifSIlIiUiwCHAIS0hLSFtIG0gjB+MH3Iech4dHR0dmRuZG9UZ1RnbF9sXwBXAFYgTiBM/ET8R9g72Dq0MrQxkCmQKHQgdCNYF1gWFA4UDJwEnAcD+wP5L/Ev8zfnN+Vv3W/fz9PP0lvKW8lfwV/Au7i7uGOwY7C/qL+py6HLo4Obg5pfll+WT5JPkzePN41zjXOM+4z7jZuNm4+nj6eO+5L7k0+XT5S7nLue/6L/obept6jbsNuwQ7hDu7O/s787xzvG387fzoPWg9Yv3i/d4+Xj5X/tf+z/9P/0b/xv/9AD0AMMCwwKNBI0EUgZSBgYIBgisCawJRwtHC8cMxwwwDjAOiA+ID74QvhDYEdgR2hLaErETsRNdFF0U5hTmFDsVOxVnFWcVexV7FW0VbRVIFUgVFhUWFcYUxhReFF4U6hPqE2ETYRPMEswSNBI0EooRihHMEMwQ+w/7DwoPCg/6DfoN0wzTDJELkQs1CjUKxQjFCD8HPwegBaAF7QPtAy0CLQJiAGIAkv6S/sv8y/wO+w77WflZ+bf3t/cl9iX2nvSe9DbzNvPs8ezxwPDA8MTvxO/z7vPuQO5A7rftt+1O7U7t9+z37MHsweyn7Kfsoeyh7L3svez67PrsTe1N7cPtw+1U7lTu9+737rbvtu+M8IzwcvFy8Wvya/Jw83DzcPRw9Gv1a/Vb9lv2N/c39wT4BPi9+L34WPlY+dT51Pku+i76aPpo+pD6kPqz+rP63/rf+h/7H/t4+3j75Pvk+178Xvzb/Nv8Uv1S/bz9vP0a/hr+bP5s/rf+t/4B/wH/Sv9K/5H/kf/W/9b/FgAWAFQAVACQAJAAyQDJAPkA+QAWARYBGwEbAQUBBQHaANoAqQCpAHoAegBUAFQAMgAyAAoACgDT/9P/hf+F/yH/If+q/qr+JP4k/pj9mP0J/Qn9efx5/PH78ftx+3H79/r3+of6h/od+h36t/m3+WD5YPka+Rr55vjm+Mn4yfjD+MP4zPjM+Oz47Pgk+ST5cflx+df51/lO+k76yvrK+kf7R/vA+8D7NPw0/Kj8qPwh/SH9nv2e/R/+H/6i/qL+Jf8l/6X/pf8jACMAoACgABkBGQGNAY0B+gH6AVcCVwKgAqACzgLOAtwC3ALMAswCpwKnAnUCdQI9Aj0CBAIEAsoBygGLAYsBRgFGAf8A/wC4ALgAeQB5AEMAQwAaABoA/v/+/+7/7v/s/+z/+P/4/xEAEQAzADMAWwBbAIQAhACrAKsAzwDPAOwA7AAAAQABCQEJAQgBCAH+AP4A8QDxAOEA4QDQANAAugC6AJ4AngB5AHkAUABQACYAJgABAAEA4f/h/8b/xv+s/6z/lP+U/33/ff9s/2z/Y/9j/2L/Yv9r/2v/fv9+/5z/nP/J/8n/BgAGAFMAUwCtAK0AFAEUAYUBhQEFAgUCmAKYAjkDOQPpA+kDpASkBF4FXgUYBhgG0wbTBocHhwc2CDYI4QjhCHoJegn/Cf8JdAp0CtAK0AoXCxcLTwtPC3ALcAt6C3oLcgtyC04LTgsRCxELwgrCClsKWwrgCeAJWglaCcgIyAguCC4IkweTB/IG8gZLBksGoAWgBfIE8gRCBEIEkwOTA+EC4QIpAikCZwFnAZ0AnQDO/87/Av8C/0L+Qv6T/ZP99fz1/Gv8a/zv++/7fvt++xv7G/u++r76Y/pj+gr6Cvqv+a/5TflN+ez47PiJ+In4IPgg+Ln3ufdP90/33vbe9nL2cvYJ9gn2ovWi9Ur1SvX/9P/0wPTA9Jb0lvSC9IL0fvR+9JT0lPTE9MT0CvUK9W/1b/Xw9fD1hvaG9jT3NPf29/b3yPjI+Kv5q/mg+qD6pful+7L8svzH/cf93P7c/vH/8f8LAQsBLAIsAk8DTwNyBHIEjgWOBZIGkgZ9B30HTQhNCPcI9wh1CXUJvwm/CcAJwAlsCWwJvAi8CK0HrQc8BjwGcARwBFgCWAL5//n/ZP1k/bP6s/ry9/L3K/Ur9X3yffLt7+3vgu2C7V3rXet+6X7p4efh55bmluaP5Y/lueS55CfkJ+TV49Xjt+O34+Pj4+NO5E7k5eTl5LLlsuW05rTm5Ofk51bpVukN6w3r/Oz87CLvIu988Xzx+/P786H2ofZw+XD5Yvxi/Gf/Z/98AnwCkwWTBZUIlQiAC4ALUA5QDukQ6RBHE0cTbBVsFT0XPRfFGMUYFhoWGh8bHxvwG/AbnRydHBMdEx1kHWQdqh2qHdcd1x39Hf0dLh4uHlIeUh5yHnIemx6bHrMesx7EHsQe3B7cHt8e3x7THtMewB7AHooeih41HjUezR3NHTsdOx2HHIccwhvCG94a3hrnGecZ7hjuGOIX4hfHFscWpRWlFWwUbBQgEyATyhHKEV8QXxDcDtwOQw1DDYoLigutCa0JswezB6IFogV+A34DTQFNARj/GP/Y/Nj8i/qL+kL4Qvj+9f71xvPG87jxuPHX79fvI+4j7rDssOx563nrdup26rnpuek96T3p9+j36PTo9Ogj6SPpculy6eXp5el06nTqEOsQ68jryOuW7Jbsce1x7WHuYe5j72Pvb/Bv8JDxkPHF8sXyCPQI9F31XfXD9sP2Nvg2+LT5tPk/+z/70PzQ/GD+YP7u/+7/cwFzAekC6QJOBE4EoQWhBdUG1QbsB+wH6AjoCMIJwgmCCoIKMwszC84LzgtYDFgM2QzZDEcNRw2sDawNEQ4RDm8Obw7KDsoOJw8nD3kPeQ/BD8EPBhAGEDkQORBZEFkQaRBpEFcQVxAkECQQ2A/YD2gPaA/YDtgOLw4vDmUNZQ19DH0MgAuAC3AKcApUCVQJMwgzCA8HDwfmBeYFvwS/BJoDmgN3AncCVwFXATsAOwAb/xv/9v32/dL80vyu+677ifqJ+m35bflU+FT4O/c79zH2MfY29Tb1S/RL9H3zffPF8sXyGfIZ8oPxg/EB8QHxjvCO8DzwPPAD8APw0O/Q76nvqe9+737vSO9I7xrvGu/37vfu3+7f7uLu4u777vvuIO8g71rvWu+k76Tv8e/x70zwTPCt8K3wDfEN8XnxefHz8fPxd/J38g/zD/O187XzYPRg9BT1FPXP9c/1jfaN9lH3UfcU+BT4zvjO+H/5f/kn+if6y/rL+nb7dvsv/C/89vz2/Mb9xv2Z/pn+af9p/zIAMgD1APUAsgGyAWICYgIDAwMDjgOOA/8D/wNTBFMEkASQBLIEsgS7BLsErQStBIUEhQRGBEYE9QP1A5UDlQMqAyoDuAK4AkECQQLFAcUBSwFLAdYA1gBpAGkACQAJALX/tf9r/2v/K/8r//T+9P7I/sj+p/6n/pH+kf6E/oT+fP58/nj+eP51/nX+df51/nr+ev6E/oT+lP6U/qf+p/68/rz+0P7Q/ub+5v77/vv+D/8P/yL/Iv8v/y//Nv82/zj/OP81/zX/LP8s/x7/Hv8G/wb/4P7g/qn+qf5d/l3+/v3+/ZL9kv0g/SD9q/yr/D78Pvza+9r7gvuC+zr7OvsF+wX74frh+tX61fre+t76/Pr8+jD7MPt5+3n71PvU+z/8P/y3/Lf8NP00/bP9s/0y/jL+rP6s/iL/Iv+U/5T/AgACAGkAaQDIAMgAIQEhAXEBcQG8AbwBAgICAkICQgJ7AnsCrQKtAtkC2QIAAwADJgMmA0kDSQNrA2sDjAOMA6cDpwO+A74D1QPVA+oD6gP/A/8DFwQXBC0ELQRDBEMEXQRdBHgEeASXBJcEvgS+BOcE5wQTBRMFSAVIBYMFgwXIBcgFHgYeBn4GfgbnBucGVgdWB8EHwQckCCQIfwh/CMYIxgj6CPoIGwkbCR4JHgkGCQYJ1QjVCIoIiggpCCkIuge6Bz4HPge7BrsGNgY2Bq4FrgUkBSQFmgSaBAwEDAR3A3cD2wLbAjgCOAKNAY0B3QDdAC8ALwCF/4X/4P7g/kX+Rf6z/bP9Kv0q/bD8sPxG/Eb86/vr+6L7ovtn+2f7Nvs2+xH7Efv5+vn66vrq+uf65/rs+uz67/rv+vP68/ry+vL66vrq+uD64PrP+s/6s/qz+o76jvpg+mD6Kfop+vX59fnE+cT5kvmS+V75Xvkf+R/5z/jP+Hb4dvgb+Bv4xPfE9373fvdL90v3JPck9xL3EvcV9xX3LPcs92T3ZPe897z3Mfgx+ML4wvhp+Wn5Ifoh+un66frB+8H7pfyl/JX9lf2O/o7+j/+P/5IAkgCaAZoBpwKnArADsAO5BLkEwAXABbsGuwatB60HkwiTCGIJYgkXChcKsQqxCiMLIwtqC2oLhguGC2wLbAsVCxULggqCCqoJqgmQCJAIOwc7B7cFtwUPBA8EVgJWAp0AnQDw/vD+Wv1a/ef75/uX+pf6Zvlm+Vn4Wfhn92f3iPaI9sH1wfUN9Q31ZPRk9NPz0/NX81fz7fLt8qTypPJ98n3ydPJ08pLykvLU8tTyMPMw86zzrPNF9EX09/T39Mf1x/W69rr2zvfO9wX5Bflg+mD61PvU+1f9V/3g/uD+YwBjANIB0gEoAygDYARgBG8FbwVWBlYGFwcXB60HrQciCCIIggiCCNAI0AgXCRcJXwlfCaIJognkCeQJKAooCmkKaQqtCq0K/Ar8Ck4LTguoC6gLDgwODHMMcwzWDNYMNw03DYcNhw3EDcQN9Q31DQ4ODg4XDhcOHA4cDhcOFw4QDhAOFA4UDhoOGg4sDiwOUQ5RDn8Ofw61DrUO9g72Di4PLg9aD1oPfQ99D4YPhg95D3kPWw9bDx8PHw/JDskOWw5bDs4Nzg0jDSMNZAxkDI0LjQumCqYKuQm5CcUIxQjPB88H2wbbBukF6QX5BPkEEQQRBC8DLwNSAlICeQF5AaIAogDJ/8n/7v7u/hX+Ff49/T39Z/xn/Jv7m/vV+tX6FvoW+mT5ZPnB+MH4K/gr+K/3r/dP90/3DfcN9/P28/YA9wD3Lfct93j3ePfc99z3TvhO+NL40vhl+WX5AfoB+qT6pPpG+0b74fvh+3T8dPwB/QH9jP2M/Rf+F/6h/qH+JP8k/5r/mv8AAAAAVQBVAJoAmgDUANQABAEEASkBKQFEAUQBVQFVAVsBWwFYAVgBSwFLATQBNAETARMB6wDrAL8AvwCPAI8AXgBeACoAKgD1//X/wP/A/5D/kP9n/2f/Qv9C/x7/Hv/0/vT+wf7B/or+iv5Z/ln+N/43/in+Kf4x/jH+SP5I/mr+av6S/pL+v/6//vT+9P4t/y3/Zf9l/5X/lf+5/7n/zP/M/9H/0f/K/8r/uf+5/5//n/94/3j/RP9E/wT/BP+5/rn+Zf5l/gr+Cv6o/aj9QP1A/dX81fxq/Gr8AfwB/KD7oPtD+0P75/rn+oz6jPos+iz6xfnF+Vz5XPnz+PP4jviO+Dj4OPjz9/P3vve+9573nveP94/3ifeJ95H3kfej96P3ufe599X31ffy9/L3BvgG+BX4Ffgg+CD4Kfgp+Dz4PPhg+GD4kPiQ+ND40PgW+Rb5V/lX+ZX5lfnO+c75//n/+TD6MPpg+mD6ivqK+q36rfrD+sP6xvrG+r36vfqp+qn6kPqQ+n/6f/p5+nn6fvp++pD6kPqp+qn6wfrB+tb61vro+uj69/r3+g37Dfsy+zL7avtq+7r7uvsg/CD8lPyU/BD9EP2N/Y39Bf4F/nj+eP7o/uj+V/9X/8r/yv9EAEQAxQDFAEgBSAHHAccBOAI4Ao4CjgLAAsACzALMArcCtwKNAo0CYAJgAjsCOwIiAiICDwIPAvUB9QHGAcYBfwF/ASYBJgHOAM4AjQCNAHEAcQB4AHgAlgCWALUAtQC+AL4AoQChAF0AXQD2//b/ff99/wj/CP+v/q/+hf6F/pX+lf7X/tf+N/83/5X/lf/Y/9j/8//z/+3/7f/d/93/3P/c//3//f9CAEIAnwCfAP8A/wBSAVIBjAGMAagBqAGrAasBnQGdAYgBiAF3AXcBcAFwAXMBcwF5AXkBegF6AW4BbgFeAV4BWQFZAW0BbQGjAaMB8AHwATcCNwJcAlwCTQJNAg0CDQK6AboBfQF9AXMBcwGrAasBHQIdAqwCrAI7AzsDsgOyA/8D/wMeBB4EFQQVBOsD6wO0A7QDhQOFA3EDcQOJA4kDzgPOAy4ELgSRBJEE2gTaBPcE9wToBOgExATEBKAEoASRBJEEmwSbBK0ErQSuBK4EigSKBDcENwS+A74DNgM2A7ICsgJBAkEC5wHnAZsBmwFUAVQBCQEJAbgAuABlAGUAFgAWANT/1P+h/6H/fP98/2H/Yf9K/0r/Mv8y/xj/GP/6/vr+2P7Y/rP+s/6L/ov+Yv5i/kH+Qf41/jX+Rv5G/nn+ef7C/sL+Df8N/z//P/9H/0f/JP8k/+f+5/6r/qv+hf6F/oD+gP6M/oz+kP6Q/nP+c/4q/ir+u/27/UT9RP3i/OL8qfyp/J38nfyy/LL8z/zP/OT85Pzq/Or84fzh/Nf81/zU/NT83fzd/PP88/wW/Rb9Rf1F/Yb9hv3Y/dj9Of45/qD+oP4E/wT/Xv9e/63/rf/9//3/VQBVALkAuQApASkBmwGbAQYCBgJlAmUCvQK9Ag8DDwNgA2ADqwOrA+UD5QMDBAMEAwQDBOUD5QO2A7YDhAOEA1cDVwM2AzYDIgMiAxYDFgMQAxADCgMKA/wC/ALeAt4CrgKuAmsCawIbAhsCyAHIAXgBeAEsASwB5ADkAKIAogBsAGwATQBNAEsASwBjAGMAgwCDAJIAkgCBAIEATgBOAAwADADU/9T/vf+9/8v/y//3//f/LQAtAGAAYACKAIoAqwCrAMMAwwDTANMA1wDXANEA0QDJAMkAzADMAN4A3gD/AP8AIwEjAT8BPwFNAU0BUQFRAVQBVAFcAVwBbwFvAYcBhwGlAaUBzAHMAf0B/QE0AjQCZgJmAoECgQJ2AnYCRQJFAvYB9gGbAZsBRAFEAfYA9gCuAK4AaABoACQAJADm/+b/s/+z/4v/i/9m/2b/Pv8+/xH/Ef/g/uD+sP6w/oT+hP5S/lL+E/4T/sL9wv1l/WX9DP0M/c38zfyz/LP8vvy+/Oj86Pwk/ST9aP1o/bH9sf0C/gL+XP5c/sD+wP4t/y3/oP+g/xIAEgCAAIAA5QDlADwBPAGJAYkB0gHSARoCGgJkAmQCrwKvAvIC8gImAyYDSANIA1cDVwNWA1YDSANIAy4DLgMHAwcD1gLWApwCnAJgAmACJAIkAugB6AGsAawBbwFvATMBMwH3APcAvAC8AIIAggBGAEYACgAKANT/1P+w/7D/qP+o/8D/wP/3//f/RABEAJ0AnQD8APwAXQFdAboBugESAhICZgJmArgCuAIPAw8DdQN1A+wD7AN0BHQEBwUHBZUFlQUTBhMGewZ7Br8GvwbgBuAG4QbhBsYGxgaeBp4GegZ6Bl8GXwZSBlIGTQZNBkAGQAYgBiAG6gXqBZsFmwU3BTcFwgTCBD0EPQSvA68DIgMiA6ACoAIvAi8C1AHUAYYBhgE8ATwB7gDuAJ8AnwBTAFMAEgASAOH/4f+8/7z/m/+b/3j/eP9Q/1D/Iv8i//L+8v7E/sT+nP6c/oL+gv58/nz+if6J/qb+pv7J/sn+4/7j/u3+7f7l/uX+zf7N/qz+rP6D/oP+TP5M/gb+Bv6y/bL9Vf1V/f78/vyz/LP8c/xz/D38PfwC/AL8tfu1+1f7V/vk+uT6Yfph+t753vlm+Wb5BvkG+c34zfi9+L34yvjK+Oj46PgA+QD5APkA+ez47PjK+Mr4pvim+JH4kfiQ+JD4oPig+MX4xfj7+Pv4PPk8+Yv5i/nh+eH5Nfo1+oT6hPrI+sj6/fr9+ij7KPtM+0z7aftp+4n7ifuz+7P76vvq+zT8NPyK/Ir83fzd/B/9H/1F/UX9Tf1N/UT9RP05/Tn9NP00/T39Pf1O/U79Yf1h/XL9cv2C/YL9k/2T/av9q/3J/cn97f3t/Rj+GP5N/k3+jv6O/t3+3f43/zf/lv+W//L/8v9HAEcAkACQAMgAyADsAOwA9AD0AN8A3wCvAK8AbgBuACcAJwDk/+T/rP+s/3//f/9e/17/R/9H/z3/Pf88/zz/Pf89/zb/Nv8Z/xn/5P7k/p3+nf5T/lP+FP4U/uj96P3K/cr9q/2r/YL9gv1N/U39Ef0R/d383fy4/Lj8n/yf/JH8kfyI/Ij8ifyJ/KX8pfzo/Oj8Vv1W/eX95f2A/oD+D/8P/4n/if/w//D/UABQALEAsQARAREBZwFnAaYBpgHMAcwB4gHiAfQB9AERAhECPAI8Am4CbgKgAqACzwLPAvkC+QIjAyMDTwNPA3UDdQOOA44DmAOYA44DjgN0A3QDTgNOAxwDHAPjAuMCqQKpAnkCeQJfAl8CZAJkAoYChgK6AroC9AL0AiYDJgNLA0sDZwNnA3cDdwN+A34DfQN9A3QDdANsA2wDbwNvA34DfgOUA5QDqQOpA68DrwOhA6EDhwOHA2YDZgNGA0YDKwMrAxIDEgP7AvsC6QLpAtsC2wLLAssCrwKvAngCeAIeAh4CpgGmASIBIgGkAKQAOAA4AOH/4f+Y/5j/Vf9V/xn/Gf/m/ub+vP68/pb+lv5t/m3+O/47/gb+Bv7b/dv9xP3E/cT9xP3O/c79zP3M/bf9t/2M/Yz9WP1Y/S39Lf0S/RL9CP0I/RP9E/0z/TP9af1p/bP9s/0B/gH+OP44/kT+RP4g/iD+3f3d/Zz9nP12/Xb9b/1v/Xv9e/2C/YL9c/1z/VT9VP02/Tb9J/0n/Sz9LP09/T39TP1M/Vn9Wf1r/Wv9iv2K/bv9u/30/fT9J/4n/kj+SP5b/lv+av5q/oj+iP7B/sH+FP8U/3v/e//v/+//agBqAOQA5ABYAVgBvgG+AQ8CDwJPAk8CiAKIAsQCxAIPAw8DaQNpA8gDyAMfBB8EZQRlBI4EjgSbBJsEjgSOBG0EbQRDBEMEHQQdBP4D/gPmA+YDzwPPA6kDqQNvA28DJwMnA9sC2wKWApYCXwJfAjICMgIFAgUC1AHUAZ4BngFjAWMBJgEmAeAA4ACOAI4AMAAwANT/1P+I/4j/W/9b/03/Tf9X/1f/a/9r/4T/hP+h/6H/wv/C/+H/4f/u/+7/4P/g/7j/uP+J/4n/bv9u/3j/eP+h/6H/z//P/+r/6v/r/+v/5P/k/+7/7v8YABgAVQBVAIcAhwCUAJQAfQB9AFkAWQBKAEoAYgBiAJQAlADEAMQA1QDVAMUAxQCoAKgAmwCbAKwArADZANkAEwETAU0BTQGHAYcBxwHHAQgCCAI8AjwCTgJOAi8CLwLqAeoBmwGbAWMBYwFTAVMBaAFoAY4BjgG2AbYB2QHZAfcB9wEQAhACIAIgAhsCGwL+Af4B1AHUAbABsAGfAZ8BngGeAZgBmAF6AXoBPAE8Ae8A7wCtAK0AkACQAJsAmwC9AL0A3wDfAO8A7wDpAOkA1gDWAMEAwQC1ALUAtwC3AMgAyADiAOIA+AD4APkA+QDUANQAjACMADUANQDw//D/2P/Y/+3/7f8UABQAIgAiAAEAAQC2/7b/Yv9i/yz/LP8e/x7/KP8o/yn/Kf8O/w7/3P7c/rD+sP6l/qX+vf69/uT+5P7+/v7+/f79/vH+8f73/vf+Iv8i/2z/bP+2/7b/4f/h/97/3v/D/8P/s/+z/87/zv8aABoAggCCAOYA5gAsASwBTwFPAV4BXgFxAXEBnAGcAeAB4AE2AjYCiwKLAswCzALxAvEC/QL9AvoC+gL3AvcC/QL9AggDCAMNAw0D/wL/AtcC1wKcApwCXgJeAioCKgIFAgUC7AHsAdAB0AGnAacBcAFwAS8BLwHwAPAAvAC8AJkAmQCFAIUAdwB3AGEAYQA2ADYA8f/x/53/nf9Q/1D/JP8k/yP/I/9B/0H/YP9g/2P/Y/8//z//CP8I/+L+4v7k/uT+Df8N/0H/Qf9b/1v/Sv9K/x3/Hf/y/vL+4/7j/uz+7P70/vT+2f7Z/pP+k/40/jT+2/3b/aP9o/2J/Yn9b/1v/Tv9O/3f/N/8a/xr/AD8APy3+7f7k/uT+4n7ifuD+4P7b/tv+0/7T/su+y77FfsV+w/7D/sa+xr7Kvsq+zr7OvtC+0L7QPtA+z77PvtF+0X7Xftd+4r7ivvH+8f7BvwG/D/8P/x0/HT8q/yr/PP88/xM/Uz9rf2t/Qb+Bv5R/lH+k/6T/tr+2v4y/zL/lv+W//T/9P87ADsAZwBnAIUAhQCpAKkA2gDaAA0BDQEuAS4BMAEwARkBGQECAQIBAAEAARIBEgEqASoBMwEzASgBKAEYARgBFQEVASUBJQE2ATYBLgEuAQIBAgHAAMAAigCKAHQAdAB4AHgAdQB1AEwATAD5//n/mv+a/1n/Wf9L/0v/Xv9e/2j/aP9I/0j/Af8B/7X+tf6M/oz+j/6P/qv+q/65/rn+pv6m/n7+fv5g/mD+Xf5d/m7+bv50/nT+Vv5W/hj+GP7a/dr9uv26/cT9xP3f/d/96v3q/dn92f25/bn9q/2r/cn9yf0N/g3+Xf5d/qH+of7U/tT+Bf8F/0f/R/+a/5r/7v/u/y8ALwBWAFYAdQB1AJ0AnQDXANcAFwEXAUcBRwFcAVwBZAFkAXcBdwGkAaQB6AHoASgCKAJPAk8CWgJaAlcCVwJbAlsCbwJvAoMCgwKIAogCeQJ5Al8CXwJNAk0CTAJMAk4CTgJBAkECHAIcAusB6wHEAcQBuwG7AccBxwHRAdEBxQHFAZ8BnwF1AXUBYQFhAWoBagGBAYEBjwGPAYQBhAFoAWgBUgFSAU4BTgFXAVcBWQFZAUIBQgEUARQB4gDiAMAAwACyALIApQClAIYAhgBLAEsAAwADAMb/xv+j/6P/lP+U/4f/h/9t/23/R/9H/yb/Jv8V/xX/Ef8R/wn/Cf/p/un+r/6v/m7+bv48/jz+JP4k/iH+If4e/h7+DP4M/u/97/3V/dX9yf3J/c39zf3X/df93f3d/eP94/3z/fP9Fv4W/kz+TP6D/oP+rP6s/sP+w/7R/tH+5P7k/gD/AP8c/xz/Jv8m/xf/F//3/vf+2v7a/s3+zf7P/s/+0P7Q/sT+xP6r/qv+lv6W/pX+lf6t/q3+0P7Q/ur+6v7x/vH+7f7t/vD+8P4H/wf/MP8w/1//X/+I/4j/rP+s/9X/1f8NAA0AUwBTAJkAmQDTANMAAwEDATIBMgFxAXEBxgHGASMCIwJ2AnYCswKzAtsC2wL9Av0CJAMkA0sDSwNlA2UDagNqA1kDWQNCA0IDNQM1AzEDMQMpAykDDgMOA9wC3AKgAqACawJrAkQCRAIgAiAC6wHrAZgBmAExATEBzgDOAIUAhQBaAFoAOQA5AA0ADQDJ/8n/eP94/zH/Mf8C/wL/5P7k/sj+yP6e/p7+aP5o/jf+N/4f/h/+Jf4l/j7+Pv5V/lX+X/5f/mP+Y/5w/nD+jf6N/rf+t/7g/uD++/77/gr/Cv8b/xv/O/87/2z/bP+i/6L/0P/Q/+//7/8GAAYAJQAlAFQAVACNAI0AwgDCAOUA5QD4APgABwEHAR8BHwFHAUcBegF6AasBqwHRAdEB7gHuAQkCCQImAiYCRwJHAmACYAJpAmkCYwJjAlICUgI/Aj8CMwIzAisCKwIkAiQCGwIbAgsCCwL1AfUB2wHbAb4BvgGiAaIBjAGMAX4BfgF5AXkBegF6AXsBewF3AXcBcAFwAWkBaQFnAWcBaQFpAWkBaQFfAV8BTQFNATcBNwEoASgBJQElAS0BLQE3ATcBOwE7ATcBNwEwATABMAEwATkBOQFIAUgBUwFTAVABUAE9AT0BIQEhAQQBBAHoAOgAygDKAKYApgB4AHgAQgBCAAwADADa/9r/rv+u/4b/hv9c/1z/Lv8u/wD/AP/U/tT+qv6q/oP+g/5d/l3+Nv42/hT+FP74/fj95P3k/dr92v3X/df92P3Y/eL94v33/ff9F/4X/kP+Q/50/nT+pv6m/tj+2P4M/wz/Q/9D/3z/fP+z/7P/4f/h/wMAAwAdAB0ANQA1AFUAVQCAAIAAswCzAOYA5gAUARQBOgE6AVwBXAF/AX8BpQGlAcoBygHoAegB+gH6AfwB/AHvAe8B2gHaAb4BvgGdAZ0BegF6AVIBUgEmASYB+gD6AM4AzgCnAKcAhwCHAHAAcABhAGEAWgBaAFUAVQBOAE4AQQBBADAAMAAbABsABgAGAPX/9f/o/+j/3//f/9j/2P/T/9P/0P/Q/9D/0P/U/9T/3P/c/+n/6f/6//r/EgASAC4ALgBLAEsAZABkAHIAcgBwAHAAXgBeAEIAQgAfAB8A+P/4/8//z/+g/6D/aP9o/yr/Kv/o/uj+p/6n/mv+a/42/jb+Bf4F/tb91v2m/ab9dP10/UL9Qv0Q/RD93/zf/LP8s/yM/Iz8avxq/FH8UfxA/ED8M/wz/Cz8LPwp/Cn8K/wr/DX8NfxJ/En8ZPxk/If8h/yx/LH83/zf/BT9FP1Q/VD9k/2T/dr92v0j/iP+af5p/qr+qv7n/uf+If8h/1v/W/+V/5X/zv/O/wIAAgAtAC0ATwBPAGkAaQB8AHwAiwCLAJYAlgCeAJ4AoACgAJwAnACTAJMAiwCLAIQAhACDAIMAhwCHAI0AjQCTAJMAlgCWAJcAlwCXAJcAlgCWAJUAlQCRAJEAiACIAHsAewBrAGsAXABcAE8ATwBEAEQAOgA6AC4ALgAgACAAEAAQAP/////u/+7/3//f/9D/0P/D/8P/t/+3/67/rv+o/6j/oP+g/5X/lf+D/4P/af9p/0r/Sv8q/yr/DP8M//P+8/7d/t3+y/7L/r7+vv63/rf+tv62/rv+u/7G/sb+0/7T/uL+4v70/vT+Cf8J/yH/If8+/z7/XP9c/3v/e/+Z/5n/t/+3/9T/1P/w//D/DAAMACYAJgBAAEAAWgBaAHUAdQCSAJIArwCvAMoAygDfAN8A7QDtAPQA9AD2APYA+QD5APwA/AD/AP8AAQEBAf4A/gDzAPMA5QDlANQA1ADHAMcAwQDBAL8AvwDBAMEAwgDCAL8AvwC7ALsAtwC3ALcAtwC5ALkAvgC+AMEAwQDBAMEAwQDBAMIAwgDHAMcA0wDTAOMA4wD1APUABQEFAREBEQEZARkBHwEfASQBJAEpASkBLQEtAS4BLgErASsBIgEiARIBEgH9AP0A5ADkAMgAyACpAKkAiACIAGUAZQBBAEEAHQAdAPr/+v/a/9r/vf+9/6P/o/+J/4n/cP9w/1n/Wf9F/0X/Nf81/yr/Kv8k/yT/H/8f/x3/Hf8d/x3/IP8g/yj/KP80/zT/Qv9C/1D/UP9d/13/Z/9n/3L/cv99/33/iv+K/5j/mP+l/6X/sf+x/7z/vP/I/8j/0//T/9v/2//e/97/2//b/9X/1f/M/8z/xP/E/73/vf+1/7X/qP+o/5b/lv+C/4L/b/9v/2H/Yf9Z/1n/Uv9S/0z/TP9E/0T/P/8//0L/Qv9Q/1D/aP9o/4T/hP+g/6D/u/+7/9j/2P/8//z/JwAnAFkAWQCMAIwAvQC9AOkA6QAUARQBQgFCAXABcAGaAZoBvAG8AdIB0gHiAeIB8wHzAQcCBwIfAh8CNAI0Aj8CPwI+Aj4CNAI0AicCJwIbAhsCEgISAgUCBQLwAfAB0gHSAa4BrgGJAYkBZwFnAUQBRAEbARsB6QDpAKwArABrAGsALQAtAPf/9//H/8f/nP+c/3H/cf9H/0f/H/8f//3+/f7h/uH+yP7I/rL+sv6c/pz+i/6L/oH+gf6B/oH+hv6G/oz+jP6P/o/+kf6R/pb+lv6k/qT+u/67/tf+1/7y/vL+B/8H/xf/F/8l/yX/N/83/07/Tv9p/2n/gv+C/5b/lv+n/6f/uf+5/8//z//p/+n/AwADABgAGAAmACYALgAuADUANQA/AD8ATQBNAF8AXwBvAG8AeAB4AHgAeABuAG4AXgBeAEwATABAAEAAPAA8AEEAQQBJAEkATwBPAEsASwA/AD8ALwAvACAAIAAWABYAEwATABIAEgARABEADgAOAAwADAAOAA4AFgAWACIAIgAvAC8APQA9AE0ATQBjAGMAgACAAKEAoQDCAMIA3ADcAO0A7QD4APgABgEGARgBGAExATEBTAFMAWIBYgFvAW8BdgF2AXkBeQF7AXsBfgF+AX0BfQF3AXcBawFrAVsBWwFKAUoBOQE5ASYBJgEPAQ8B8wDzANEA0QCsAKwAhQCFAFsAWwAwADAABQAFANv/2/+0/7T/kf+R/3H/cf9S/1L/M/8z/xn/Gf8I/wj/Av8C/wb/Bv8P/w//Fv8W/xz/HP8i/yL/Lf8t/0H/Qf9e/17/gf+B/6b/pv/K/8r/7f/t/w8ADwAvAC8ATQBNAGYAZgB/AH8AnQCdAMEAwQDsAOwAFQEVATEBMQE9AT0BOgE6ATEBMQEpASkBJwEnAScBJwEiASIBEwETAfkA+QDcANwAvwC/AKUApQCKAIoAagBqAEUARQAdAB0A9//3/9b/1v+3/7f/l/+X/3f/d/9b/1v/SP9I/0L/Qv9H/0f/UP9Q/1f/V/9a/1r/Xf9d/2X/Zf90/3T/h/+H/5f/l/+j/6P/rP+s/7j/uP/L/8v/4//j//r/+v8IAAgADAAMAAgACAACAAIA/v/+//j/+P/t/+3/2P/Y/7v/u/+a/5r/ef95/1n/Wf81/zX/DP8M/93+3f6t/q3+gv6C/l/+X/5C/kL+KP4o/g7+Dv72/fb94f3h/dT91P3N/c39yf3J/cX9xf3A/cD9vf29/b79vv3H/cf91P3U/ef95/0A/gD+H/4f/kf+R/53/nf+qf6p/tz+3P4M/wz/O/87/2v/a/+e/57/z//P//v/+/8dAB0ANwA3AE8ATwBqAGoAiwCLAK8ArwDQANAA6gDqAPwA/AAIAQgBEgESARoBGgEeAR4BHwEfARsBGwEUARQBDQENAQYBBgH7APsA7QDtANwA3ADMAMwAvwC/ALcAtwCwALAApgCmAJYAlgCAAIAAbABsAFsAWwBPAE8ARQBFADYANgAfAB8AAQABAOD/4P/D/8P/qf+p/5T/lP+C/4L/cP9w/1//X/9P/0//Qf9B/zP/M/8k/yT/Ef8R//3+/f7n/uf+0P7Q/rz+vP6q/qr+nf6d/pj+mP6c/pz+qP6o/rr+uv7M/sz+3P7c/uv+6/79/v3+Ev8S/y7/Lv9P/0//bv9u/4r/iv+j/6P/vP+8/9f/1//4//j/HQAdAEAAQABfAF8AeAB4AIwAjACeAJ4AsgCyAMgAyADdAN0A7gDuAPgA+AD6APoA9QD1AOwA7ADjAOMA2wDbANYA1gDRANEAzADMAMIAwgC3ALcAqwCrAKQApACjAKMApwCnAK0ArQCvAK8ArACsAKMAowCZAJkAlwCXAJ0AnQCrAKsAvwC/ANAA0ADdAN0A5gDmAOsA6wDuAO4A8QDxAPIA8gD0APQA+QD5AAQBBAEUARQBJgEmAS4BLgEpASkBFQEVAfoA+gDiAOIA1QDVAM8AzwDJAMkAuAC4AJcAlwBrAGsAPwA/AB8AHwALAAsA/f/9/+v/6//P/8//p/+n/3//f/9e/17/Sv9K/0D/QP86/zr/Mf8x/yT/JP8Z/xn/FP8U/xz/HP8u/y7/Rf9F/1v/W/9q/2r/cf9x/3T/dP95/3n/g/+D/5X/lf+r/6v/vf+9/8j/yP/G/8b/u/+7/63/rf+i/6L/nP+c/5v/m/+a/5r/lP+U/4r/iv99/33/cf9x/2j/aP9h/2H/Wv9a/1L/Uv9K/0r/Rf9F/0X/Rf9K/0r/UP9Q/1b/Vv9d/13/av9q/4H/gf+j/6P/y//L//D/8P8MAAwAIAAgADIAMgBHAEcAZgBmAIwAjAC1ALUA2gDaAPkA+QATARMBLQEtAUYBRgFcAVwBbgFuAXgBeAF7AXsBeAF4AW4BbgFcAVwBQgFCAR8BHwH6APoA2QDZAMIAwgCxALEAoQChAIsAiwBtAG0ASQBJACUAJQAJAAkA8v/y/93/3f/D/8P/ov+i/37/fv9f/1//SP9I/zr/Ov8w/zD/I/8j/xP/E/8E/wT//v7+/gX/Bf8a/xr/Nf81/0//T/9j/2P/cf9x/3//f/+P/4//pP+k/7n/uf/L/8v/2P/Y/+L/4v/r/+v/9//3/wQABAAPAA8AFAAUABMAEwARABEAEwATABkAGQAjACMAKgAqACsAKwAlACUAHgAeABsAGwAfAB8AKgAqADcANwBBAEEARgBGAEoASgBPAE8AWABYAGEAYQBlAGUAXgBeAE4ATgA5ADkAKAAoAB4AHgAbABsAGAAYAA8ADwD9//3/5f/l/8v/y/+2/7b/p/+n/5z/nP+U/5T/jf+N/4b/hv9//3//dv92/23/bf9l/2X/Yf9h/2X/Zf9y/3L/gv+C/5D/kP+a/5r/of+h/6//r//N/83//P/8/zMAMwBlAGUAiACIAJwAnACtAK0AxQDFAOUA5QAEAQQBFAEUAQ4BDgH6APoA7QDtAPQA9AANAQ0BJQElASIBIgH5APkAuwC7AIoAigCFAIUArwCvAOoA6gAHAQcB5wDnAJAAkAAqACoA6P/o/+X/5f8UABQARgBGAEwATAAPAA8ApP+k/zj/OP/4/vj+8/7z/hn/Gf9G/0b/Wf9Z/0j/SP8h/yH/+/77/uz+7P74/vj+Gv8a/0P/Q/9r/2v/i/+L/6X/pf+7/7v/0v/S/+v/6/8LAAsAMAAwAFkAWQB/AH8AngCeALIAsgC/AL8AygDKANgA2ADqAOoA+QD5AP4A/gDyAPIA1wDXALQAtACTAJMAfwB/AHsAewCEAIQAkQCRAJQAlACDAIMAWQBZACAAIADo/+j/xv/G/8f/x//l/+X/DQANACcAJwAkACQABgAGAN//3//F/8X/w//D/9T/1P/o/+j/8//z//b/9v/9//3/EwATADcANwBaAFoAaQBpAFgAWAAuAC4AAAAAAOT/5P/h/+H/8f/x/wUABQAQABAAEAAQAAYABgD3//f/5P/k/8n/yf+o/6j/if+J/3f/d/93/3f/hP+E/5D/kP+K/4r/b/9v/0f/R/8i/yL/Cv8K/wH/Af/+/v7+9/73/ur+6v7e/t7+3v7e/u3+7f4I/wj/I/8j/zT/NP84/zj/Nv82/zn/Of9I/0j/Yf9h/4D/gP+c/5z/sP+w/7r/uv+6/7r/tP+0/63/rf+p/6n/q/+r/7P/s/+8/7z/xv/G/9D/0P/e/97/8v/y/wkACQAeAB4AJgAmAB8AHwAOAA4A/////wEAAQAYABgAPQA9AF0AXQBoAGgAWABYADYANgAQABAA+f/5//f/9/8HAAcAHQAdAC4ALgAzADMAKgAqABcAFwABAAEA7f/t/9//3//X/9f/1P/U/9T/1P/T/9P/0P/Q/8v/y/+//7//rP+s/5D/kP9x/3H/V/9X/07/Tv9Z/1n/dP90/5D/kP+d/53/lf+V/37/fv9p/2n/Z/9n/3v/e/+g/6D/xf/F/9//3//t/+3/9//3/wcABwAgACAAPwA/AFkAWQBqAGoAdAB0AHsAewCFAIUAkQCRAJsAmwCgAKAApACkAKcApwCuAK4AtQC1ALUAtQCrAKsAnACcAI0AjQCHAIcAiACIAIsAiwCFAIUAdgB2AGMAYwBVAFUAUQBRAFUAVQBVAFUASgBKADMAMwAdAB0AEwATABoAGgAuAC4AQwBDAFAAUABWAFYAXABcAGkAaQB9AH0AlQCVAKkAqQC3ALcAwwDDANMA0wDoAOgA/gD+AAwBDAENAQ0BBgEGAfsA+wDzAPMA7QDtAOYA5gDWANYAwADAAKgAqACUAJQAhwCHAHsAewBqAGoAUABQADAAMAARABEA+f/5/+b/5v/S/9L/uf+5/5v/m/9+/37/aP9o/17/Xv9c/1z/W/9b/1f/V/9R/1H/Tf9N/1H/Uf9b/1v/Zv9m/2v/a/9s/2z/a/9r/3D/cP9+/37/kv+S/6T/pP+u/67/r/+v/67/rv+w/7D/uf+5/8j/yP/W/9b/4P/g/+L/4v/h/+H/4P/g/+L/4v/k/+T/4v/i/9v/2//O/87/v/+//7T/tP+y/7L/uf+5/8b/xv/R/9H/1f/V/9D/0P/F/8X/vP+8/7n/uf++/77/x//H/83/zf/O/87/zv/O/9H/0f/f/9//9//3/xMAEwArACsAOQA5AD8APwBCAEIASgBKAFcAVwBmAGYAcQBxAHQAdAByAHIAcABwAG8AbwBuAG4AaABoAFcAVwA9AD0AIQAhAAoACgD7//v/8//z/+r/6v/b/9v/xf/F/6z/rP+W/5b/hP+E/3n/ef9w/3D/af9p/2P/Y/9h/2H/Yv9i/2X/Zf9n/2f/Z/9n/2T/ZP9j/2P/Zf9l/2z/bP93/3f/hP+E/5P/k/+j/6P/tP+0/8T/xP/S/9L/3P/c/+T/5P/r/+v/9P/0//3//f8FAAUACgAKAAwADAAMAAwADQANABAAEAASABIAEQARAA0ADQAHAAcAAwADAAIAAgAEAAQABgAGAAUABQAAAAAA+//7//v/+/8AAAAACwALABUAFQAbABsAHgAeACAAIAAjACMAKgAqADIAMgA4ADgAOgA6ADgAOAA0ADQAMgAyADEAMQAwADAALgAuACoAKgAmACYAIwAjACIAIgAhACEAHwAfABwAHAAZABkAFwAXABUAFQASABIADAAMAAIAAgD3//f/7v/u/+j/6P/m/+b/5v/m/+f/5//m/+b/5v/m/+f/5//r/+v/8P/w//X/9f/6//r/AAAAAAYABgAOAA4AGAAYACMAIwAuAC4AOQA5AEQARABOAE4AWQBZAGEAYQBmAGYAaQBpAGsAawBtAG0AbwBvAG8AbwBrAGsAYQBhAFMAUwBEAEQAOAA4AC8ALwAoACgAIQAhABcAFwAKAAoA+v/6/+n/6f/a/9r/zf/N/8P/w/+7/7v/tP+0/6//r/+r/6v/p/+n/6L/ov+e/57/nP+c/57/nv+k/6T/rv+u/7r/uv/I/8j/1v/W/+P/4//u/+7/+//7/woACgAbABsALgAuAEEAQQBSAFIAXwBfAGgAaABvAG8AdgB2AH8AfwCJAIkAkgCSAJgAmACYAJgAlQCVAI4AjgCHAIcAgACAAHsAewB2AHYAcQBxAGwAbABoAGgAZABkAGAAYABbAFsAVQBVAE4ATgBHAEcAQQBBADwAPAA4ADgANAA0ADEAMQAtAC0AKQApACUAJQAhACEAHAAcABgAGAAVABUAFgAWABoAGgAfAB8AIgAiACEAIQAaABoAEQARAAgACAADAAMAAQABAAEAAQD+//7/9v/2/+v/6//f/9//1f/V/87/zv/L/8v/yP/I/8T/xP+//7//t/+3/6//r/+n/6f/oP+g/5z/nP+b/5v/mf+Z/5j/mP+V/5X/kP+Q/4z/jP+K/4r/jP+M/5H/kf+Y/5j/nP+c/5//n/+g/6D/ov+i/6f/p/+v/6//uf+5/8H/wf/G/8b/yP/I/8n/yf/M/8z/0f/R/9f/1//b/9v/2//b/9n/2f/V/9X/0v/S/9H/0f/S/9L/1v/W/9v/2//i/+L/6v/q//P/8//7//v/AQABAAYABgAJAAkADAAMABEAEQAZABkAIgAiACwALAA0ADQAOQA5ADwAPAA+AD4AQABAAEQARABIAEgATgBOAFIAUgBTAFMAUQBRAEsASwBBAEEANQA1ACkAKQAgACAAGQAZABQAFAAPAA8ABwAHAP3//f/v/+//4P/g/9L/0v/F/8X/uf+5/67/rv+i/6L/lf+V/4r/iv+A/4D/eP94/3L/cv9s/2z/Z/9n/2X/Zf9l/2X/av9q/3T/dP+A/4D/jP+M/5j/mP+i/6L/qv+q/7P/s/+7/7v/xf/F/8//z//a/9r/4//j/+v/6//w//D/8//z//f/9//8//z/BQAFAA4ADgAYABgAHgAeACIAIgAjACMAJQAlACgAKAAsACwAMAAwADIAMgAyADIAMgAyADMAMwA3ADcAPQA9AEEAQQBDAEMAQQBBAD4APgA8ADwAPQA9AEMAQwBLAEsAUwBTAFsAWwBhAGEAZgBmAGoAagBtAG0AbwBvAG8AbwBwAHAAcwBzAHYAdgB5AHkAeQB5AHYAdgBwAHAAaQBpAGMAYwBeAF4AWgBaAFYAVgBRAFEASgBKAEMAQwA7ADsAMwAzACkAKQAfAB8AEwATAAcABwD8//z/8v/y/+n/6f/g/+D/2v/a/9T/1P/Q/9D/zv/O/8v/y//I/8j/xP/E/7//v/+7/7v/uP+4/7f/t/+2/7b/tv+2/7b/tv+1/7X/tP+0/7P/s/+0/7T/tf+1/7b/tv+2/7b/tP+0/7P/s/+x/7H/sv+y/7T/tP+3/7f/uf+5/7j/uP+z/7P/qv+q/6P/o/+f/5//ov+i/6j/qP+w/7D/tf+1/7b/tv+z/7P/sf+x/7T/tP+7/7v/xv/G/8//z//X/9f/3P/c/+L/4v/t/+3//f/9/w8ADwAgACAALQAtADUANQA9AD0ASABIAFYAVgBlAGUAcgByAHgAeAB3AHcAcgByAG4AbgBtAG0AbwBvAHEAcQBwAHAAagBqAF8AXwBSAFIARQBFADsAOwA0ADQALwAvACsAKwAmACYAIAAgABkAGQASABIACgAKAAMAAwD+//7//P/8//v/+//7//v/+v/6//n/+f/3//f/9//3//n/+f/8//z/AQABAAQABAAHAAcACgAKAA8ADwAVABUAHQAdACQAJAAqACoALgAuADAAMAAwADAAMAAwAC8ALwAsACwAKAAoACMAIwAeAB4AGgAaABYAFgARABEADAAMAAcABwACAAIA/v/+//v/+//5//n/9v/2//L/8v/u/+7/6//r/+n/6f/o/+j/5f/l/+L/4v/d/93/2v/a/9j/2P/Y/9j/2v/a/9r/2v/Z/9n/2P/Y/9j/2P/b/9v/3v/e/+H/4f/j/+P/4v/i/+H/4f/h/+H/5P/k/+f/5//r/+v/7P/s/+v/6//r/+v/7P/s/+//7//x//H/8v/y//D/8P/u/+7/7P/s/+7/7v/x//H/9f/1//j/+P/4//j/+P/4//n/+f/8//z//////wMAAwAGAAYACQAJAAwADAAPAA8AEgASABUAFQAWABYAFgAWABYAFgAYABgAGwAbAB0AHQAeAB4AHQAdABsAGwAZABkAGAAYABkAGQAYABgAFgAWABIAEgANAA0ABwAHAAEAAQD8//z/9v/2//L/8v/v/+//7P/s/+v/6//p/+n/5v/m/+H/4f/d/93/2f/Z/9n/2f/a/9r/2//b/93/3f/e/97/4P/g/+P/4//n/+f/6//r//D/8P/y//L/9f/1//f/9//5//n//P/8//////8BAAEAAgACAAMAAwAFAAUACQAJAA4ADgATABMAFwAXABoAGgAbABsAGwAbABsAGwAaABoAGgAaABsAGwAbABsAGgAaABgAGAAUABQAEAAQAAwADAAKAAoACgAKAAoACgALAAsACwALAAwADAAOAA4AEQARABUAFQAYABgAGQAZABgAGAAWABYAFgAWABcAFwAaABoAHAAcABsAGwAYABgAFAAUABEAEQAPAA8ADgAOAA0ADQALAAsABwAHAAMAAwD//////v/+//3//f/7//v/+P/4//T/9P/w//D/7P/s/+r/6v/o/+j/5v/m/+T/5P/h/+H/3//f/97/3v/d/93/3P/c/9v/2//Z/9n/2P/Y/9f/1//X/9f/2f/Z/9r/2v/b/9v/3P/c/9z/3P/d/93/3f/d/97/3v/f/9//3//f/97/3v/b/9v/2f/Z/9f/1//W/9b/1v/W/9f/1//Y/9j/1//X/9T/1P/R/9H/z//P/8//z//S/9L/1v/W/9n/2f/d/93/4P/g/+P/4//n/+f/7P/s//L/8v/4//j//////wYABgAMAAwAEgASABgAGAAeAB4AJAAkACoAKgAwADAANgA2ADoAOgA+AD4AQQBBAEQARABHAEcASwBLAE8ATwBQAFAAUABQAE0ATQBJAEkAQwBDAD4APgA5ADkANAA0AC4ALgAnACcAHwAfABUAFQANAA0ABQAFAP/////6//r/9v/2//L/8v/u/+7/6v/q/+j/6P/o/+j/6f/p/+r/6v/s/+z/7f/t/+7/7v/v/+//8f/x//T/9P/3//f/+//7/wAAAAAGAAYACwALAA8ADwARABEAEgASABIAEgASABIAFAAUABYAFgAYABgAGAAYABUAFQARABEADQANAAoACgAJAAkACAAIAAcABwAEAAQAAAAAAPz//P/4//j/9P/0//H/8f/v/+//7P/s/+r/6v/p/+n/6f/p/+r/6v/r/+v/7P/s/+7/7v/v/+//8f/x//P/8//1//X/9//3//n/+f/8//z/AAAAAAQABAAIAAgACgAKAAoACgAIAAgACAAIAAkACQALAAsADAAMAA0ADQAMAAwACgAKAAkACQAIAAgACQAJAAkACQAIAAgABwAHAAYABgAGAAYABgAGAAUABQAEAAQAAQABAP7//v/9//3//f/9//7//v8AAAAAAQABAAEAAQABAAEAAAAAAAEAAQACAAIABAAEAAcABwAKAAoADAAMAA8ADwARABEAEgASABEAEQAQABAADgAOAA0ADQAMAAwADAAMAA0ADQAOAA4ADQANAAsACwAJAAkABQAFAAEAAQD9//3/+v/6//f/9//1//X/8f/x/+3/7f/p/+n/5f/l/+L/4v/h/+H/4f/h/+L/4v/i/+L/4//j/+P/4//k/+T/5f/l/+j/6P/q/+r/7f/t//D/8P/0//T/9//3//v/+///////AwADAAcABwAKAAoADgAOABEAEQAUABQAFAAUABMAEwARABEADwAPAA0ADQAMAAwADAAMAAoACgAIAAgABgAGAAMAAwACAAIAAgACAAEAAQD//////P/8//n/+f/2//b/9f/1//b/9v/3//f/+P/4//j/+P/3//f/9//3//f/9//4//j/+f/5//n/+f/5//n/+v/6//z//P8AAAAABAAEAAgACAAMAAwADgAOABEAEQAUABQAFwAXABoAGgAdAB0AHgAeAB0AHQAdAB0AHAAcABwAHAAcABwAHAAcABoAGgAYABgAFQAVABMAEwARABEAEAAQAA8ADwANAA0ACgAKAAgACAAGAAYABgAGAAUABQAEAAQAAAAAAPz//P/4//j/9P/0//H/8f/v/+//7v/u/+z/7P/q/+r/6P/o/+f/5//m/+b/5P/k/+P/4//i/+L/4P/g/97/3v/c/9z/2//b/9r/2v/a/9r/2v/a/9v/2//b/9v/3P/c/9z/3P/e/97/3//f/+H/4f/j/+P/5P/k/+X/5f/l/+X/5v/m/+f/5//p/+n/7P/s/+//7//y//L/9f/1//f/9//7//v//////wIAAgAFAAUABwAHAAgACAAKAAoADAAMAA4ADgAQABAAEQARABAAEAAOAA4ADQANAA0ADQANAA0ADgAOAA0ADQALAAsACAAIAAUABQACAAIAAQABAAIAAgACAAIAAwADAAMAAwADAAMAAgACAAEAAQAAAAAAAAAAAAAAAAABAAEAAgACAAMAAwADAAMAAwADAAIAAgACAAIAAQABAAEAAQACAAIAAwADAAQABAAGAAYABwAHAAcABwAHAAcABwAHAAYABgAGAAYABQAFAAMAAwAAAAAA/v/+//3//f/9//3//f/9//3//f/9//3//P/8//v/+//6//r/+v/6//r/+v/6//r/+f/5//j/+P/2//b/9f/1//T/9P/0//T/9P/0//T/9P/0//T/9v/2//j/+P/7//v//v/+/wEAAQACAAIAAwADAAUABQAHAAcACQAJAAoACgALAAsADAAMAAwADAAOAA4AEQARABMAEwATABMAEwATABEAEQAQABAAEQARABMAEwAUABQAFAAUABMAEwARABEADgAOAAwADAALAAsACwALAAoACgAIAAgABwAHAAUABQAFAAUABQAFAAUABQAGAAYABgAGAAYABgAGAAYABgAGAAcABwAIAAgACAAIAAgACAAIAAgABwAHAAcABwAGAAYABQAFAAUABQAFAAUABQAFAAUABQAFAAUABAAEAAMAAwABAAEAAAAAAP7//v/8//z/+//7//v/+//8//z//P/8//z//P/7//v/+v/6//r/+v/6//r/+//7//3//f//////AQABAAIAAgADAAMAAwADAAQABAAGAAYABwAHAAgACAAJAAkACQAJAAoACgAKAAoACgAKAAkACQAIAAgABwAHAAYABgAFAAUABQAFAAQABAADAAMAAQABAP/////8//z/+//7//n/+f/3//f/9v/2//X/9f/z//P/8v/y//D/8P/v/+//7v/u/+7/7v/u/+7/7v/u/+7/7v/u/+7/8P/w//H/8f/z//P/9v/2//j/+P/6//r//P/8//7//v8BAAEAAwADAAUABQAHAAcABwAHAAgACAAJAAkACgAKAA0ADQAQABAAEgASABMAEwATABMAEwATABIAEgASABIAEQARABEAEQAQABAAEAAQABAAEAAQABAADwAPAA8ADwAPAA8ADgAOAA4ADgANAA0ADQANAAsACwAJAAkABwAHAAYABgAFAAUABAAEAAQABAADAAMAAgACAAEAAQAAAAAA///////////+//7//v/+//3//f/9//3//P/8//r/+v/5//n/9//3//T/9P/y//L/8P/w/+7/7v/s/+z/6v/q/+j/6P/n/+f/5v/m/+X/5f/k/+T/4//j/+L/4v/h/+H/4P/g/+D/4P/i/+L/4//j/+T/5P/l/+X/5f/l/+b/5v/o/+j/6//r/+//7//z//P/9v/2//j/+P/6//r//f/9/wAAAAAEAAQACAAIAAsACwAMAAwADQANAA4ADgAPAA8AEAAQABAAEAAPAA8ADgAOAAwADAAKAAoACQAJAAkACQAJAAkACAAIAAcABwAFAAUAAwADAAIAAgACAAIAAgACAAIAAgABAAEAAAAAAAAAAAD//////v/+//3//f/8//z/+v/6//n/+f/5//n/+v/6//v/+//7//v//P/8//3//f/+//7/AQABAAQABAAHAAcACQAJAAoACgAKAAoACwALAA0ADQAPAA8AEAAQABEAEQARABEAEAAQAA8ADwAPAA8ADwAPAA4ADgAMAAwACgAKAAgACAAGAAYABQAFAAQABAACAAIAAAAAAP3//f/7//v/+v/6//r/+v/6//r/+f/5//j/+P/3//f/9//3//f/9//3//f/+P/4//j/+P/3//f/9//3//b/9v/3//f/+P/4//r/+v/7//v//f/9//7//v8AAAAAAgACAAMAAwAFAAUABgAGAAcABwAHAAcABgAGAAYABgAGAAYABgAGAAcABwAIAAgACAAIAAcABwAFAAUABAAEAAIAAgACAAIAAwADAAQABAAEAAQAAwADAAEAAQD//////f/9//z//P/7//v/+//7//v/+//6//r/+f/5//j/+P/4//j/9//3//f/9//3//f/9//3//f/9//3//f/9//3//f/9//4//j/+P/4//n/+f/6//r/+v/6//v/+//7//v//P/8//3//f//////AAAAAAAAAAABAAEAAQABAAEAAQACAAIAAwADAAMAAwAEAAQABQAFAAUABQAGAAYABgAGAAcABwAJAAkACgAKAAsACwALAAsACwALAAkACQAIAAgABwAHAAYABgAGAAYABwAHAAgACAAIAAgACAAIAAcABwAHAAcABgAGAAUABQAEAAQAAwADAAEAAQAAAAAA///////////+//7//v/+//z//P/7//v/+f/5//n/+f/5//n/+v/6//v/+//7//v/+//7//r/+v/7//v/+//7//3//f/9//3//v/+//3//f/9//3//P/8//z//P/9//3//f/9//7//v/+//7///////////8AAAAAAAAAAAEAAQABAAEAAgACAAIAAgACAAIAAgACAAMAAwAEAAQABQAFAAUABQAFAAUABQAFAAUABQAGAAYABgAGAAYABgAGAAYABQAFAAQABAAEAAQABAAEAAQABAAFAAUABQAFAAUABQAEAAQAAwADAAIAAgADAAMABAAEAAQABAAFAAUABQAFAAUABQAEAAQABAAEAAMAAwACAAIAAQABAAAAAAD////////////////+//7//v/+//3//f/8//z//P/8//z//P/9//3//f/9//3//f/8//z/+//7//r/+v/6//r/+v/6//r/+v/6//r/+f/5//j/+P/3//f/9//3//f/9//4//j/+P/4//j/+P/3//f/+P/4//j/+P/5//n/+v/6//v/+//7//v/+//7//v/+//7//v//P/8//3//f/+//7//v/+//3//f/8//z/+//7//v/+//7//v/+//7//z//P/8//z//P/8//z//P/9//3//f/9//3//f/9//3//f/9//7//v/+//7//////wEAAQACAAIAAwADAAUABQAGAAYABgAGAAcABwAHAAcABwAHAAgACAAIAAgACAAIAAgACAAIAAgACAAIAAcABwAIAAgACAAIAAgACAAJAAkACQAJAAgACAAIAAgABwAHAAYABgAFAAUAAwADAAAAAAD9//3/+v/6//f/9//1//X/9f/1//b/9v/3//f/+P/4//b/9v/0//T/8f/x/+//7//v/+//8P/w//P/8//1//X/9f/1//X/9f/0//T/9P/0//X/9f/4//j/+//7//3//f/9//3//f/9//z//P/+//7/AQABAAQABAAGAAYABgAGAAQABAADAAMABAAEAAcABwAKAAoACwALAAoACgAHAAcABwAHAAgACAAMAAwADwAPABAAEAAOAA4ADAAMAAsACwALAAsADgAOAA8ADwAPAA8ADQANAAsACwALAAsADAAMAA0ADQANAA0ACwALAAgACAAGAAYABgAGAAcABwAHAAcABgAGAAQABAACAAIAAgACAAQABAAFAAUABgAGAAQABAACAAIAAAAAAAAAAAABAAEAAQABAAEAAQD//////f/9//z//P/9//3////////////9//3/+//7//n/+f/5//n/+v/6//v/+//6//r/+f/5//j/+P/5//n//P/8//////8CAAIAAgACAAIAAgACAAIABAAEAAYABgAIAAgACAAIAAcABwAFAAUABAAEAAQABAAFAAUABQAFAAQABAACAAIAAQABAAAAAAAAAAAAAAAAAP/////+//7//f/9//v/+//7//v/+v/6//n/+f/4//j/9//3//f/9//4//j/+P/4//j/+P/3//f/9v/2//f/9//5//n/+//7//3//f/+//7//v/+//////8BAAEAAwADAAUABQAGAAYABgAGAAUABQAGAAYACAAIAAsACwANAA0ADgAOAA4ADgAOAA4ADwAPABAAEAARABEAEQARABAAEAAOAA4ADQANAA0ADQANAA0ADgAOAA0ADQALAAsACQAJAAcABwAGAAYABQAFAAQABAADAAMAAgACAAIAAgACAAIAAwADAAQABAAEAAQAAwADAAIAAgABAAEAAQABAAEAAQAAAAAAAAAAAP////////////////7//v/9//3/+//7//n/+f/4//j/9//3//f/9//3//f/+P/4//j/+P/5//n/+f/5//j/+P/2//b/8//z//H/8f/x//H/8f/x//P/8//0//T/9P/0//P/8//x//H/8f/x//L/8v/z//P/8//z//L/8v/w//D/7v/u/+z/7P/r/+v/6//r/+r/6v/q/+r/6v/q/+v/6//u/+7/8P/w//L/8v/0//T/9P/0//X/9f/3//f/+v/6//z//P/+//7//v/+//7//v/+//7//v/+//////8BAAEAAgACAAMAAwAFAAUABwAHAAoACgAMAAwADQANAA0ADQALAAsACgAKAAkACQAJAAkACQAJAAkACQAJAAkACQAJAAsACwANAA0ADwAPABAAEAARABEAEQARABAAEAARABEAEgASABMAEwASABIAEQARAA4ADgALAAsACAAIAAYABgAFAAUAAwADAAIAAgAAAAAA//////3//f/6//r/+P/4//b/9v/0//T/8v/y//L/8v/y//L/8v/y//H/8f/x//H/8P/w/+7/7v/u/+7/7f/t/+3/7f/u/+7/7//v//D/8P/w//D/8P/w//D/8P/v/+//7v/u/+//7//v/+//8P/w//D/8P/w//D/8P/w//D/8P/w//D/8P/w/+//7//u/+7/7f/t/+3/7f/u/+7/8P/w//T/9P/4//j/+//7//7//v8AAAAAAgACAAMAAwAFAAUABgAGAAcABwAIAAgACAAIAAgACAAIAAgACQAJAAoACgAMAAwADgAOAA8ADwAPAA8AEAAQABEAEQASABIAEwATABUAFQAWABYAFgAWABUAFQAVABUAFQAVABYAFgAWABYAFQAVABMAEwAPAA8ADAAMAAgACAAHAAcABgAGAAcABwAHAAcACAAIAAgACAAIAAgACAAIAAgACAAIAAgABgAGAAUABQADAAMAAwADAAIAAgACAAIAAgACAAEAAQD//////f/9//z//P/9//3//v/+/wAAAAACAAIAAwADAAQABAADAAMAAgACAAAAAAD+//7//f/9//3//f8AAAAAAwADAAcABwAIAAgACAAIAAYABgADAAMAAQABAAAAAAD///////////3//f/8//z/+//7//v/+//7//v/+v/6//r/+v/7//v//f/9/wEAAQAGAAYACQAJAAsACwALAAsACgAKAAkACQAJAAkACQAJAAgACAAGAAYABAAEAAIAAgABAAEAAQABAAMAAwAFAAUABgAGAAgACAAJAAkACwALAAwADAANAA0ADAAMAAoACgAIAAgABgAGAAUABQAEAAQAAgACAP/////8//z/+v/6//n/+f/6//r/+//7//r/+v/4//j/9f/1//L/8v/w//D/7//v//H/8f/z//P/9f/1//j/+P/7//v//v/+/wEAAQADAAMABQAFAAYABgAFAAUABAAEAAEAAQD+//7/+//7//f/9//0//T/8v/y//L/8v/z//P/9P/0//b/9v/3//f/+f/5//r/+v/7//v//P/8//v/+//4//j/8//z/+7/7v/q/+r/5//n/+j/6P/q/+r/7f/t/+//7//y//L/9f/1//j/+P/8//z//////wEAAQACAAIAAQABAP/////8//z/+P/4//X/9f/z//P/8v/y//P/8//2//b/+P/4//r/+v/5//n/9//3//X/9f/2//b/+P/4//v/+//9//3/+//7//f/9//y//L/7//v//D/8P/3//f/AQABAAkACQAPAA8AEAAQAA0ADQAKAAoACQAJAAkACQALAAsADgAOAA8ADwAOAA4ADAAMAAkACQAHAAcACAAIAAwADAASABIAGAAYABsAGwAbABsAFwAXABIAEgAOAA4ADAAMAAsACwAKAAoACQAJAAgACAAHAAcABwAHAAcABwAIAAgABgAGAAIAAgD8//z/9v/2//D/8P/s/+z/6P/o/+f/5//n/+f/6P/o/+r/6v/r/+v/6f/p/+X/5f/h/+H/4f/h/+X/5f/u/+7/9v/2//v/+//6//r/9v/2//H/8f/x//H/9//3/wAAAAALAAsAFQAVAB8AHwAoACgAMQAxADkAOQA/AD8AQgBCAEUARQBJAEkATQBNAFAAUABPAE8ASQBJAEEAQQA6ADoAOQA5AD0APQBCAEIAQwBDAD8APwA2ADYALgAuACoAKgAtAC0AMQAxADIAMgAuAC4AJAAkABkAGQARABEADQANAA4ADgAQABAAEgASABIAEgAPAA8ACgAKAAIAAgD5//n/8v/y/+//7//x//H/9f/1//n/+f/3//f/8v/y/+z/7P/q/+r/7f/t//b/9v8AAAAABwAHAAkACQAKAAoACwALAA4ADgASABIAFwAXABkAGQAZABkAGAAYABUAFQARABEADQANAAgACAAGAAYABQAFAAYABgAFAAUAAQABAPr/+v/0//T/8v/y//f/9/8AAAAACgAKAA4ADgAMAAwABQAFAP3//f/3//f/8f/x/+n/6f/f/9//0//T/8j/yP+//7//uP+4/6//r/+l/6X/mf+Z/5H/kf+P/4//lP+U/5z/nP+h/6H/of+h/5//n/+g/6D/p/+n/7P/s//A/8D/yP/I/8n/yf/H/8f/xv/G/8v/y//T/9P/2v/a/93/3f/b/9v/1v/W/9H/0f/N/83/y//L/8v/y//N/83/0v/S/9v/2//l/+X/7//v//X/9f/6//r/AQABAAwADAAaABoAJgAmACsAKwAnACcAHQAdABUAFQASABIAFAAUABQAFAAOAA4AAgACAPX/9f/t/+3/7v/u//X/9f/9//3/AAAAAP7//v/6//r/+f/5//r/+v/9//3//v/+//z//P/6//r//P/8/wMAAwAMAAwAEgASABEAEQALAAsABAAEAAEAAQADAAMABAAEAAIAAgD5//n/7v/u/+X/5f/i/+L/5P/k/+r/6v/u/+7/8v/y//X/9f/5//n//////wIAAgD/////9//3/+3/7f/l/+X/4f/h/+D/4P/f/9//2//b/9b/1v/T/9P/1f/V/9r/2v/e/97/3//f/9v/2//W/9b/1f/V/9n/2f/h/+H/5//n/+n/6f/m/+b/5P/k/+b/5v/v/+///f/9/w0ADQAbABsAJQAlACsAKwAvAC8ALwAvAC0ALQApACkAJQAlACQAJAAmACYAKQApACoAKgArACsALwAvADkAOQBLAEsAYABgAHAAcAB1AHUAbgBuAF4AXgBOAE4AQwBDAD0APQA8ADwAOgA6ADgAOAA2ADYAOAA4AD4APgBGAEYATgBOAFMAUwBUAFQAUgBSAFAAUABLAEsARABEADsAOwAyADIALQAtACoAKgApACkAJAAkABoAGgALAAsA/P/8//L/8v/v/+//7//v/+z/7P/i/+L/0//T/8L/wv+3/7f/tf+1/7z/vP/J/8n/2f/Z/+j/6P/0//T/+//7//v/+//3//f/8//z//L/8v/3//f//v/+/wEAAQD7//v/7f/t/9//3//a/9r/5f/l//v/+/8VABUAJwAnAC8ALwAvAC8AMAAwADcANwBCAEIATQBNAFEAUQBOAE4ARgBGAD8APwA7ADsAOgA6ADwAPAA/AD8ARQBFAE0ATQBVAFUAWQBZAFMAUwBEAEQAMQAxACIAIgAZABkAGAAYABgAGAAWABYAEQARAA8ADwARABEAGgAaACIAIgAiACIAFwAXAAMAAwDs/+z/2P/Y/8r/yv++/77/sP+w/6D/oP+Q/5D/hP+E/3z/fP94/3j/df91/3L/cv9x/3H/dP90/3z/fP+G/4b/jf+N/5D/kP+R/5H/lf+V/6D/oP+y/7L/yP/I/97/3v/0//T/CwALACAAIAAwADAANQA1ACwALAAYABgAAwADAPT/9P/u/+7/7P/s/+f/5//g/+D/3P/c/+f/5/8IAAgAOQA5AGwAbACQAJAAnACcAJQAlACDAIMAcABwAF4AXgBHAEcAKgAqAAwADAD3//f/+P/4/xAAEAA3ADcAXwBfAH8AfwCTAJMAngCeAKAAoACSAJIAaQBpABkAGQCd/53/9/73/i/+L/5Q/VD9aPxo/Jb7lvsM+wz7A/sD+6v7q/sP/Q/9//7//hYBFgHYAtgC3APcA/ED8QMvAy8D7gHuAZ0AnQCO/47/4P7g/nn+ef4k/iT+tv22/Tf9N/3d/N388Pzw/Jz9nP3T/tP+RgBGAIsBiwFQAlACegJ6AioCKgKkAaQBKgEqAeQA5ADcANwABgEGAVEBUQGoAagB9wH3AS0CLQJHAkcCVwJXAncCdwK4ArgCDgMOA0kDSQMsAywDnQKdArEBsQGtAK0A2//b/2H/Yf8v/y//Fv8W/+7+7v69/r3+qP6o/t/+3/52/3b/WQBZAFUBVQE1AjUC0gLSAg4DDgPWAtYCJgImAhYBFgHj/+P/5P7k/mD+YP5l/mX+v/6//hv/G/85/zn/Ff8V/97+3v7D/sP+z/7P/tv+2/62/rb+VP5U/uf95/26/br9+v36/ZD+kP4y/zL/mv+a/7j/uP+6/7r/3P/c/zQANACaAJoA0gDSALwAvAB3AHcATQBNAHQAdADdAN0AQQFBAVEBUQHzAPMAVwBXAM3/zf+F/4X/b/9v/1H/Uf/7/vv+eP54/gj+CP72/fb9Wv5a/hH/Ef/V/9X/bgBuAM4AzgAKAQoBOwE7AWQBZAF4AXgBcAFwAVkBWQFMAUwBUwFTAVwBXAFDAUMB7gDuAGcAZwDM/8z/OP84/63+rf4S/hL+XP1c/az8rPxI/Ej8c/xz/Dj9OP1b/lv+c/9z/yoAKgBqAGoAWABYAC4ALgAMAAwA7f/t/8D/wP+N/43/e/97/7H/sf8tAC0AvAC8ABMBEwEHAQcBqQCpADoAOgD3//f/7v/u//v/+//v/+//wP/A/5P/k/+g/6D/AgACAJgAmAAjASMBaAFoAV0BXQEnAScB+AD4AOsA6wD8APwAHwEfAU4BTgGUAZQB9AH0AVcCVwKUApQChwKHAjECMQK7AbsBXQFdASsBKwEOAQ4B2ADYAG8AbwDe/97/Uf9R/+b+5v6S/pL+Kv4q/pL9kv3j/OP8Yvxi/Fj8WPzT/NP8lv2W/UD+QP6M/oz+gf6B/mb+Zv5+/n7+1f7V/jH/Mf9B/0H/3P7c/if+J/55/Xn9H/0f/TH9Mf2C/YL90f3R/fv9+/0L/gv+Lv4u/oX+hf4M/wz/nv+e/wwADABAAEAAPgA+ACAAIAD+//7/6v/q/+//7/8UABQAXwBfANAA0ABjAWMBCwILAroCugJeA14D3gPeAxoEGgT/A/8DkwOTA/4C/gKAAoACUQJRAnYCdgLEAsQC+gL6AugC6AKYApgCPgI+AhICEgIjAiMCUgJSAmkCaQJNAk0CEgISAuoB6gH7AfsBOAI4AmwCbAJbAlsC8gHyAVEBUQG0ALQASQBJABMAEwD4//j/2v/a/7P/s/+P/4//e/97/2//b/9P/0//C/8L/6v+q/5V/lX+Mv4y/lD+UP6Z/pn+4P7g/gP/A//2/vb+xP7E/nv+e/4e/h7+q/2r/S/9L/3C/ML8g/yD/IP8g/yx/LH85fzl/P/8//z+/P78+/z7/B39Hf1z/XP96f3p/Vf+V/6X/pf+nf6d/nn+ef5D/kP+Ef4R/u/97/3l/eX9+/37/Tb+Nv6P/o/+7/7v/jn/Of9X/1f/R/9H/xn/Gf/f/t/+oP6g/l3+Xf4c/hz+9f31/Qr+Cv5r/mv+Cf8J/7H/sf8oACgAUABQADUANQAIAAgA+f/5/x4AHgBuAG4A0QDRADQBNAGSAZIB5wHnASICIgIuAi4C/AH8AZwBnAEyATIB6QDpAM4AzgDNAM0AxwDHAKwArACSAJIAoACgAPAA8AB0AXQB/gH+AVsCWwJ+An4CgQKBAosCiwK2ArYC+QL5AjADMANBA0EDKgMqA/sC+wK/Ar8CdwJ3AhcCFwKqAaoBVwFXAU4BTgGkAaQBOAI4ArwCvALpAukCrwKvAjoCOgLZAdkBugG6AcgByAG/Ab8BZAFkAbkAuQAFAAUApP+k/8n/yf9fAF8AHAEcAbQBtAEMAgwCOAI4AlUCVQJpAmkCWQJZAgQCBAJqAWoBtgC2ACUAJQDT/9P/rf+t/3v/e/8V/xX/gP6A/vL98v2j/aP9o/2j/c39zf3j/eP9w/3D/YL9gv1d/V39iv2K/Qv+C/6j/qP++/77/t/+3/5V/lX+mv2a/f78/vy0/LT8wfzB/A39Df10/XT92f3Z/S3+Lf5X/lf+Pv4+/tH90f0S/RL9Ivwi/Dr7OvuN+o36N/o3+kH6Qfqn+qf6Yfth+2v8a/yu/a799/73/v7//v97AHsAUABQAJr/mv+o/qj+2f3Z/Xb9dv2b/Zv9NP40/hj/GP8UABQA+gD6AJsBmwHWAdYBoQGhARkBGQF3AHcAAwADAOf/5/8jACMAjwCPAPwA/ABVAVUBowGjAfwB/AFfAl8CpwKnAqACoAI1AjUCjAGMAf4A/gDuAO4AhgGGAZkCmQLDA8MDnwSfBPcE9wTcBNwEgwSDBBkEGQSmA6YDGAMYA14CXgKJAYkByADIAFEAUQBCAEIAjgCOAA4BDgGTAZMB/wH/AUACQAJSAlICNwI3AvEB8QGMAYwBIAEgAcEAwQB/AH8AWwBbAE4ATgBQAFAAXABcAG4AbgB2AHYAXgBeABQAFACj/6P/Mf8x//r++v4q/yr/wP/A/4kAiQA2ATYBgQGBAU8BTwG1ALUA5P/k/w7/Dv9M/kz+o/2j/Rj9GP22/Lb8lvyW/M78zvxh/WH9L/4v/gT/BP+m/6b/8f/x/+L/4v+U/5T/MP8w/9n+2f6e/p7+e/57/mf+Z/5e/l7+Zf5l/oP+g/6z/rP+3f7d/uP+4/6x/rH+Vv5W/v39/f3b/dv9Cv4K/n/+f/4N/w3/fv9+/7T/tP+t/63/fP98/zP/M//X/tf+a/5r/gH+Af69/b39yP3I/TX+Nf74/vj+4v/i/8AAwAB4AXgBBQIFAm0CbQKnAqcCnQKdAjQCNAJ2AXYBlwCXAOD/4P+O/47/pv+m//j/+P9AAEAATwBPACgAKADx//H/1v/W/+v/6/8mACYAcwBzAMUAxQAnAScBogGiAS8CLwKuAq4C+AL4AvAC8AKfAp8CJgImAqoBqgFDAUMB9QD1AL8AvwCjAKMAqwCrANUA1QAJAQkBGAEYAdgA2ABCAEIAgP+A/+P+4/61/rX+E/8T/9b/1v+tAK0ARAFEAXIBcgFHAUcB9wD3ALIAsgCKAIoAbgBuAEEAQQD7//v/tf+1/5z/nP/M/8z/NwA3AKgAqADcANwAswCzAEMAQwDJ/8n/gv+C/3//f/+d/53/nv+e/1v/W//d/t3+Xf5d/hv+G/42/jb+lv6W/gj/CP9h/2H/mv+a/87/zv8gACAAlACUAAsBCwFXAVcBWwFbASABIAHDAMMAZwBnABQAFADB/8H/ZP9k/wn/Cf/R/tH+2P7Y/h//H/9+/37/uf+5/6n/qf9U/1T/7P7s/q/+r/7I/sj+N/83/9b/1v95AHkAAgECAWUBZQGiAaIBtQG1AZQBlAE5ATkBqgCqAAQABABr/2v/Af8B/9X+1f7k/uT+IP8g/3X/df/P/8//GgAaAEIAQgA5ADkAAAAAAKb/pv9E/0T/8/7z/r3+vf6e/p7+jP6M/oP+g/6J/on+q/6r/u/+7/5N/03/r/+v//////8rACsANAA0AB8AHwD3//f/w//D/4z/jP9d/13/Rv9G/1T/VP+C/4L/uP+4/9f/1//H/8f/h/+H/y3/Lf/c/tz+q/6r/qL+ov64/rj+5f7l/iP/I/95/3n/5//n/2MAYwDYANgAMAEwAV0BXQFfAV8BPgE+AQABAAGqAKoARwBHAOz/7P+2/7b/uv+6//P/8/9FAEUAhgCGAJ4AngCPAI8AdwB3AHEAcQCAAIAAjQCNAHcAdwA1ADUA2//b/5L/kv94/3j/jv+O/7f/t//V/9X/4P/g/+r/6v8NAA0AUQBRAJ4AngDSANIA1QDVALIAsgCKAIoAeQB5AIAAgACEAIQAZwBnACUAJQDW/9b/o/+j/5z/nP+x/7H/vv++/6f/p/9z/3P/Rf9F/0D/QP9q/2r/qv+q/9j/2P/h/+H/1v/W/9b/1v/6//r/OAA4AG0AbQBxAHEAOwA7AOT/5P+T/5P/af9p/2n/af+F/4X/qf+p/87/zv/3//f/JAAkAEoASgBRAFEAJwAnAM7/zv9f/1///f79/sX+xf6//r/+4/7j/ib/Jv97/3v/2P/Y/y8ALwBrAGsAegB6AFUAVQAOAA4Axv/G/6D/oP+o/6j/0//T/wAAAAAPAA8A9f/1/7//v/+J/4n/bv9u/3v/e/+r/6v/6v/q/yYAJgBRAFEAaQBpAHAAcABrAGsAXABcAEIAQgAkACQAEAAQABUAFQA9AD0AgwCDANIA0gAJAQkBEAEQAeQA5ACYAJgATwBPACUAJQAlACUARQBFAHIAcgCgAKAAzADMAPgA+AAiASIBPQE9ATQBNAH8APwAmwCbAC8ALwDc/9z/v/+//9z/3P8iACIAdQB1ALgAuADfAN8A7ADsAOkA6QDjAOMA4wDjAO0A7QD7APsABwEHAQMBAwHnAOcAswCzAGsAawAcABwA0//T/5n/mf9y/3L/ZP9k/3L/cv+k/6T/+f/5/2kAaQDeAN4APQE9AXEBcQFvAW8BPwE/AfAA8ACUAJQAOgA6AOv/6/+u/67/iv+K/4D/gP+P/4//rf+t/8r/yv/Y/9j/0P/Q/7T/tP+Q/5D/cf9x/2b/Zv90/3T/mP+Y/8n/yf/4//j/GQAZACMAIwAUABQA7v/u/7b/tv9r/2v/EP8Q/6v+q/5I/kj++P34/dH90f3g/eD9I/4j/o3+jf4H/wf/d/93/8b/xv/m/+b/zv/O/4b/hv8c/xz/rv6u/lr+Wv45/jn+Uv5S/pf+l/7w/vD+Rf9F/4v/i//K/8r/DAAMAFIAUgCRAJEAtAC0AKwArACBAIEATABMACsAKwAxADEAWABYAIYAhgCcAJwAjACMAFoAWgAaABoA4f/h/7n/uf+m/6b/qf+p/8f/x/8IAAgAbABsAOMA4wBUAVQBpgGmAdAB0AHfAd8B6AHoAfYB9gH/Af8B6QHpAZ4BngEfAR8BigCKAAcABwCz/7P/jv+O/4P/g/+A/4D/gv+C/53/nf/m/+b/XQBdAOAA4AA8ATwBTQFNARABEAGmAKYAPwA/APn/+f/X/9f/yP/I/77/vv+9/73/1f/V/xAAEABhAGEArwCvAN0A3QDlAOUA1wDXAMYAxgC3ALcAnACcAF0AXQD2//b/fP98/xj/GP/p/un+7f7t/gf/B/8T/xP/Bf8F//H+8f73/vf+Jf8l/2j/aP+X/5f/k/+T/2D/YP8n/yf/Gf8Z/03/Tf+4/7j/MAAwAI8AjwDGAMYA1wDXAM4AzgClAKUATwBPAMb/xv8Z/xn/cP5w/vX99f3B/cH9yf3J/fb99v0v/i/+cP5w/sX+xf45/zn/wf/B/0EAQQCZAJkAtgC2AKAAoABtAG0ANQA1AAQABADX/9f/qf+p/3n/ef9R/1H/Pf89/0b/Rv9w/3D/uv+6/x0AHQCOAI4A+AD4AEABQAFNAU0BFQEVAaQApAAcABwAof+h/1L/Uv87/zv/W/9b/6L/ov8AAAAAYgBiALUAtQDrAOsAAwEDAQQBBAH9AP0A+wD7AAABAAEHAQcBDQENARcBFwEyATIBZgFmAacBpwHcAdwB5QHlAbUBtQFZAVkB7wDvAJEAkQBEAEQA+P/4/6H/of9D/0P/AP8A//v++/5G/0b/z//P/2cAZwDiAOIALgEuAVEBUQFcAVwBVwFXATgBOAHxAPEAhACEAAcABwCb/5v/WP9Y/zz/PP8x/zH/JP8k/xD/EP8A/wD///7//g//D/8g/yD/Iv8i/xH/Ef/5/vn+6/7r/uz+7P70/vT+9/73/vP+8/75/vn+F/8X/1D/UP+I/4j/nP+c/3b/dv8i/yL/xP7E/oL+gv5o/mj+ZP5k/l/+X/5U/lT+W/5b/pb+lv4V/xX/v/+//2AAYADJAMkA7ADsANoA2gCwALAAeAB4ACYAJgCn/6f//v7+/lb+Vv7o/ej92/3b/Sr+Kv6w/rD+Pv8+/8D/wP9DAEMA2wDbAIUBhQEbAhsCZwJnAkMCQwK4AbgB9gD2ADoAOgCp/6n/SP9I/wf/B//c/tz+0f7R/vf+9/5b/1v/7v/u/44AjgAZARkBfAF8AbQBtAHLAcsByAHIAawBrAF3AXcBMgEyAe0A7QC2ALYAkQCRAHUAdQBaAFoAQABAADEAMQA7ADsAXABcAIUAhQCfAJ8AoACgAJEAkQCHAIcAkwCTALQAtADUANQA3wDfANAA0AC1ALUAowCjAKMAowCpAKkAngCeAHMAcwAtAC0A4f/h/6b/pv+D/4P/cP9w/2H/Yf9V/1X/Vf9V/3D/cP+k/6T/3v/e/wUABQAJAAkA7//v/87/zv+2/7b/p/+n/5D/kP9d/13/CP8I/6T+pP5S/lL+KP4o/jD+MP5d/l3+nv6e/uf+5/41/zX/h/+H/9n/2f8hACEAVABUAGsAawBnAGcATQBNACIAIgDp/+n/qv+q/3H/cf9M/0z/RP9E/1f/V/91/3X/kP+Q/6D/oP+p/6n/tP+0/8f/x//c/9z/6v/q/+r/6v/h/+H/3v/e/+z/7P8QABAAQgBCAHQAdACfAJ8AwQDBANgA2ADgAOAAzgDOAJwAnABPAE8A/P/8/73/vf+j/6P/r/+v/9D/0P/2//b/GAAYADwAPABrAGsAqQCpAO8A7wArASsBTgFOAVIBUgE4ATgBBgEGAcEAwQByAHIAIQAhAN//3/+9/73/wf/B/+n/6f8pACkAdAB0AL4AvgAGAQYBRQFFAXIBcgGDAYMBcQFxAUABQAH/AP8AvwC/AIkAiQBaAFoALgAuAAUABQDp/+n/5P/k//r/+v8kACQAUgBSAHcAdwCRAJEApACkALIAsgCzALMAmgCaAF8AXwANAA0AvP+8/4f/h/92/3b/e/97/3v/e/9n/2f/Q/9D/yv/K/81/zX/Zf9l/6f/p//c/9z/7v/u/9//3//B/8H/p/+n/5T/lP+A/4D/YP9g/zj/OP8Z/xn/Ff8V/zX/Nf9v/2//q/+r/9j/2P/u/+7/8f/x/+j/6P/T/9P/r/+v/3v/e/9A/0D/Ev8S///+//4P/w//N/83/2n/af+Y/5j/v/+//97/3v/0//T//P/8//L/8v/a/9r/w//D/7z/vP/Q/9D/+f/5/yYAJgBIAEgAWgBaAGMAYwBrAGsAdQB1AHUAdQBdAF0AKwArAOv/6/+1/7X/n/+f/6z/rP/S/9L//P/8/x4AHgA6ADoAXQBdAI0AjQDHAMcA/AD8ABwBHAEeAR4BBgEGAeAA4ACzALMAfwB/AEAAQADy//L/n/+f/1b/Vv8q/yr/JP8k/0P/Q/9+/37/xv/G/w8ADwBRAFEAhQCFAKgAqAC0ALQApQClAHwAfAA8ADwA7v/u/5//n/9b/1v/Lf8t/x3/Hf8m/yb/Qv9C/2T/ZP+H/4f/p/+n/8j/yP/r/+v/DAAMAB8AHwAbABsA/P/8/8r/yv+X/5f/cP9w/17/Xv9c/1z/X/9f/13/Xf9U/1T/S/9L/0j/SP9S/1L/Z/9n/4T/hP+m/6b/zP/M//f/9/8kACQAUABQAHIAcgCFAIUAhQCFAHYAdgBeAF4ARQBFAC8ALwAeAB4AEQARAAYABgD6//r/6v/q/9b/1v/D/8P/t/+3/7X/tf+9/73/y//L/9j/2P/j/+P/8P/w/wUABQAmACYATgBOAHMAcwCJAIkAjwCPAIkAiQCBAIEAgQCBAIcAhwCQAJAAlQCVAJYAlgCYAJgAogCiALUAtQDIAMgA0ADQAMQAxACmAKYAfQB9AFMAUwAvAC8AEQARAPj/+P/j/+P/1//X/9f/1//m/+b/AwADACYAJgBMAEwAbwBvAIsAiwCaAJoAmwCbAIwAjAByAHIAVwBXAEMAQwA4ADgAMAAwACUAJQASABIA/f/9/+7/7v/u/+7/+v/6/wcABwALAAsAAQABAOz/7P/Y/9j/yf/J/7//v/+x/7H/nP+c/4P/g/9v/2//av9q/3L/cv+B/4H/iv+K/4f/h/99/33/dP90/3P/c/97/3v/hv+G/5L/kv+h/6H/uf+5/93/3f8KAAoAMQAxAEgASABIAEgAOAA4AB4AHgD+//7/1P/U/5//n/9i/2L/LP8s/xL/Ev8d/x3/Sv9K/4P/g/+1/7X/1f/V/+r/6v8AAAAAIAAgAEEAQQBRAFEARABEAB0AHQDq/+r/wf/B/6z/rP+m/6b/ov+i/5X/lf+D/4P/dP90/3f/d/+L/4v/p/+n/77/vv/K/8r/zv/O/9P/0//g/+D/8//z/wcABwATABMAFQAVABIAEgAOAA4ACAAIAP7//v/q/+r/zf/N/67/rv+U/5T/hP+E/37/fv9//3//g/+D/4r/iv+W/5b/qP+o/7//v//Z/9n/8v/y/wkACQAfAB8AMwAzAEEAQQBFAEUAPQA9ACwALAAZABkACQAJAP3//f/z//P/5//n/9n/2f/R/9H/1//X//D/8P8bABsASwBLAHUAdQCQAJAAnQCdAKAAoACeAJ4AmACYAIoAigByAHIAVABUADYANgAgACAAFwAXABkAGQAfAB8AIwAjACEAIQAcABwAHAAcACYAJgA9AD0AXgBeAIUAhQCqAKoAyADIAN8A3wDvAO8A+QD5AP0A/QD4APgA6ADoAM0AzQCqAKoAhQCFAGQAZABLAEsAOQA5ACwALAAkACQAIAAgACQAJAAxADEAQwBDAFIAUgBZAFkAVgBWAE4ATgBIAEgATQBNAFwAXABtAG0AdwB3AHQAdABmAGYAVQBVAEcARwA+AD4ANQA1ACgAKAAUABQA+//7/+P/4//U/9T/zP/M/8j/yP/E/8T/vP+8/7X/tf+x/7H/sv+y/7j/uP++/77/v/+//7v/u/+z/7P/qv+q/6D/oP+X/5f/jf+N/4T/hP+A/4D/g/+D/4//j/+e/57/rP+s/7X/tf+6/7r/vf+9/8P/w//M/8z/1f/V/9f/1//Q/9D/wf/B/7L/sv+q/6r/rf+t/7r/uv/N/83/3//f/+//7//6//r///////3//f/2//b/6//r/+H/4f/f/9//6v/q/wAAAAAeAB4APQA9AFYAVgBqAGoAewB7AIsAiwCZAJkAowCjAKQApACbAJsAiQCJAHIAcgBbAFsARwBHADYANgAnACcAHQAdABgAGAAbABsAJAAkADAAMAA6ADoAQABAAEAAQAA/AD8AOwA7ADUANQApACkAGAAYAAIAAgDr/+v/2f/Z/8//z//M/8z/zf/N/9H/0f/V/9X/2v/a/93/3f/f/9//3//f/93/3f/a/9r/2P/Y/9b/1v/S/9L/x//H/7f/t/+k/6T/lP+U/43/jf+O/47/kf+R/5D/kP+G/4b/dP90/2H/Yf9W/1b/Vf9V/17/Xv9s/2z/fP98/4r/iv+Y/5j/pv+m/7X/tf/F/8X/0//T/+D/4P/r/+v/9P/0//z//P8DAAMABwAHAAkACQAFAAUA/f/9/+3/7f/X/9f/u/+7/57/nv+F/4X/d/93/3X/df9//3//j/+P/6H/of+z/7P/xP/E/9T/1P/i/+L/7P/s//D/8P/v/+//6v/q/+f/5//p/+n/8f/x//////8OAA4AGgAaACEAIQAiACIAHAAcABEAEQAEAAQA9//3/+7/7v/q/+r/7P/s//L/8v/5//n/AQABAAYABgAJAAkADAAMAA8ADwATABMAFwAXABkAGQAWABYADwAPAAMAAwD3//f/7P/s/+b/5v/k/+T/5f/l/+f/5//s/+z/9f/1/wQABAAcABwANwA3AE8ATwBeAF4AYwBjAGAAYABbAFsAVgBWAE4ATgA/AD8AJQAlAAQABADj/+P/z//P/8z/zP/Y/9j/5//n//H/8f/y//L/7f/t/+r/6v/s/+z/8f/x//L/8v/u/+7/5f/l/9//3//l/+X/9//3/xAAEAAnACcAOAA4AEMAQwBMAEwAVgBWAF4AXgBfAF8AVABUAEEAQQAtAC0AJgAmAC4ALgBEAEQAXABcAG0AbQB2AHYAeQB5AHsAewB+AH4AfwB/AHoAegBtAG0AXQBdAFEAUQBOAE4AVQBVAF8AXwBpAGkAcgByAHsAewCGAIYAkwCTAJ0AnQCgAKAAngCeAJsAmwCeAJ4ArACsAL8AvwDNAM0AzgDOAL8AvwCnAKcAjgCOAHoAegBrAGsAXQBdAEsASwA4ADgAJgAmABoAGgAVABUAEwATAA4ADgAEAAQA9v/2/+r/6v/j/+P/4v/i/+T/5P/j/+P/3//f/9j/2P/S/9L/zP/M/8b/xv++/77/sf+x/6P/o/+W/5b/i/+L/4T/hP9//3//e/97/3r/ev9+/37/if+J/5v/m/+w/7D/w//D/9D/0P/W/9b/1f/V/9D/0P/H/8f/u/+7/6v/q/+Z/5n/iP+I/3z/fP93/3f/eP94/3v/e/99/33/fv9+/3//f/+F/4X/kv+S/6X/pf+5/7n/yf/J/9D/0P/N/83/xf/F/73/vf+5/7n/uv+6/77/vv/D/8P/x//H/8n/yf/L/8v/z//P/9P/0//X/9f/2f/Z/9n/2f/X/9f/1P/U/9H/0f/M/8z/xP/E/7r/uv+y/7L/sP+w/7n/uf/N/83/5//n/wIAAgAXABcAJAAkACoAKgArACsAKgAqACYAJgAhACEAGQAZABEAEQAKAAoACQAJAA8ADwAdAB0AMQAxAEoASgBhAGEAcgByAHkAeQB0AHQAZQBlAFEAUQA/AD8AMgAyACwALAAqACoAKAAoACQAJAAfAB8AHAAcAB0AHQAiACIAJwAnACgAKAAhACEAFAAUAAQABAD1//X/6f/p/+H/4f/c/9z/2P/Y/9j/2P/a/9r/3f/d/+D/4P/g/+D/2//b/9T/1P/K/8r/wP/A/7X/tf+p/6n/nP+c/5D/kP+I/4j/if+J/5L/kv+h/6H/sf+x/77/vv/E/8T/xf/F/8P/w/+//7//uv+6/7X/tf+t/63/pP+k/5n/mf+P/4//iP+I/4b/hv+L/4v/kv+S/5r/mv+f/5//n/+f/5v/m/+W/5b/kf+R/4//j/+P/4//kv+S/5b/lv+c/5z/pv+m/7T/tP/E/8T/0f/R/9j/2P/X/9f/0f/R/8v/y//J/8n/zP/M/9D/0P/T/9P/1f/V/9f/1//e/97/7f/t/wIAAgAZABkAKwArADgAOAA/AD8AQwBDAEYARgBFAEUAPgA+ADAAMAAgACAAEwATAA4ADgAVABUAJQAlADcANwBJAEkAXABcAHEAcQCKAIoApQClAL8AvwDTANMA3gDeAOAA4ADbANsA0QDRAMAAwACqAKoAkgCSAHoAegBpAGkAYQBhAGAAYABjAGMAZgBmAGYAZgBmAGYAZwBnAGwAbAB2AHYAgwCDAI8AjwCXAJcAmgCaAJcAlwCPAI8AhQCFAHoAegBxAHEAagBqAGIAYgBYAFgASgBKADwAPAA0ADQANwA3AEYARgBcAFwAcwBzAIIAggCHAIcAhACEAHsAewBvAG8AYQBhAE8ATwA7ADsAKQApABwAHAAYABgAFwAXABcAFwAUABQADwAPAA8ADwAXABcAJwAnADkAOQBGAEYASQBJAEQARAA7ADsAMgAyACgAKAAaABoABQAFAOv/6//T/9P/xf/F/8P/w//J/8n/0P/Q/9T/1P/V/9X/2//b/+r/6v8CAAIAGgAaACsAKwAvAC8AKgAqACMAIwAeAB4AHgAeAB0AHQAXABcADAAMAP7//v/y//L/6v/q/+T/5P/b/9v/z//P/8D/wP+z/7P/qf+p/6X/pf+j/6P/of+h/6D/oP+i/6L/qf+p/7P/s/+9/73/w//D/8L/wv++/77/u/+7/7z/vP/D/8P/zf/N/9f/1//d/93/3v/e/9r/2v/S/9L/yf/J/8L/wv+9/73/vP+8/73/vf+9/73/uv+6/7T/tP+w/7D/sP+w/7b/tv/B/8H/y//L/9D/0P/S/9L/1P/U/9v/2//r/+v/AgACABoAGgAuAC4AOgA6AEAAQABDAEMARgBGAEcARwBEAEQAPQA9ADMAMwApACkAIgAiACEAIQAjACMAJgAmACgAKAAoACgAJwAnACYAJgAlACUAJAAkACMAIwAgACAAHgAeAB0AHQAfAB8AJQAlAC4ALgA6ADoARABEAE0ATQBSAFIAVQBVAFgAWABcAFwAYQBhAGUAZQBmAGYAYQBhAFkAWQBQAFAASwBLAEkASQBKAEoASgBKAEcARwBCAEIAOwA7ADIAMgAqACoAIAAgABYAFgANAA0ABQAFAAIAAgADAAMABAAEAAQABAACAAIA/f/9//j/+P/z//P/8P/w/+z/7P/o/+j/5P/k/+H/4f/f/9//3f/d/9n/2f/S/9L/y//L/8X/xf/C/8L/wv/C/8H/wf+//7//u/+7/7j/uP+8/7z/yP/I/9f/1//k/+T/6f/p/+b/5v/e/97/2P/Y/9f/1//b/9v/4v/i/+n/6f/w//D/+P/4/wQABAARABEAHQAdACMAIwAkACQAIQAhACAAIAAjACMALAAsADcANwBDAEMATgBOAFgAWABiAGIAbABsAHMAcwB2AHYAeAB4AHoAegCCAIIAjwCPAKAAoACwALAAvQC9AMUAxQDKAMoAzgDOANEA0QDRANEAzgDOAMgAyADDAMMAwwDDAMgAyADNAM0AzQDNAMQAxACxALEAmQCZAH4AfgBkAGQASwBLADEAMQAYABgAAAAAAOz/7P/g/+D/1//X/9D/0P/I/8j/vf+9/7P/s/+s/6z/qf+p/6n/qf+l/6X/mf+Z/4P/g/9k/2T/Pv8+/xX/Ff/s/uz+xf7F/qP+o/6I/oj+cf5x/l/+X/5Q/lD+QP5A/jH+Mf4k/iT+Gf4Z/hD+EP4E/gT+9P30/eH94f3N/c39u/27/bH9sf2r/av9pf2l/Z/9n/2Y/Zj9kP2Q/Y39jf2O/Y79j/2P/Y39jf2I/Yj9gP2A/X39ff2C/YL9kP2Q/aT9pP24/bj9xf3F/c79zv3U/dT92v3a/eT95P3w/fD9/P38/Qj+CP4U/hT+I/4j/jf+N/5Q/lD+a/5r/of+h/6g/qD+tv62/sv+y/7e/t7+8f7x/gX/Bf8X/xf/KP8o/zj/OP9H/0f/Vf9V/2X/Zf93/3f/if+J/5z/nP+u/67/wP/A/9T/1P/t/+3/DAAMAC8ALwBTAFMAdgB2AJYAlgCyALIAzgDOAOoA6gAGAQYBIgEiAToBOgFRAVEBZwFnAXwBfAGQAZABoQGhAasBqwGuAa4BrQGtAakBqQGnAacBpQGlAaABoAGUAZQBggGCAWwBbAFWAVYBRAFEATYBNgEqASoBHwEfARABEAH/AP8A6wDrANUA1QDAAMAArQCtAJ4AngCSAJIAigCKAIMAgwB6AHoAcABwAGUAZQBbAFsAUgBSAEgASAA6ADoAJQAlAAoACgDs/+z/0P/Q/7n/uf+m/6b/k/+T/4H/gf9v/2//Xv9e/1L/Uv9J/0n/Qv9C/zv/O/82/zb/Nf81/zv/O/9I/0j/Wf9Z/2z/bP9+/37/kf+R/6n/qf/J/8n/7v/u/xMAEwA2ADYAVQBVAHQAdACVAJUAugC6AN4A3gD+AP4AGAEYASsBKwE9AT0BUQFRAWYBZgF7AXsBjwGPAaQBpAHAAcAB6gHqASECIQJjAmMCqQKpAusC6wInAycDYANgA5UDlQPIA8gD9gP2AxwEHAQ5BDkEVARUBGwEbASFBIUEnwSfBLUEtQTKBMoE4gTiBPsE+wQaBRoFOgU6BVIFUgVbBVsFWQVZBUoFSgU3BTcFKwUrBSQFJAUjBSMFKQUpBS4FLgUzBTMFOwU7BUAFQAVABUAFOQU5BSMFIwX9BP0ExwTHBIEEgQQyBDIE5QPlA6ADoANlA2UDNgM2AwoDCgPcAtwCpwKnAm0CbQIuAi4C7QHtAacBpwFYAVgBAQEBAacApwBSAFIACgAKANL/0v+k/6T/e/97/1P/U/8u/y7/EP8Q//v++/7p/un+zv7O/qH+of5h/mH+FP4U/sv9y/2M/Yz9VP1U/SD9IP3p/On8rfyt/Hn8efxS/FL8Ofw5/Cf8J/wQ/BD87Pvs+8H7wfuU+5T7bvtu+1H7Ufs4+zj7GfsZ+/f69/rY+tj6v/q/+rT6tPqv+q/6pPqk+pL6kvp7+nv6Zvpm+mD6YPpp+mn6efp5+oj6iPqQ+pD6kvqS+pn6mfqs+qz6x/rH+ur66voL+wv7J/sn+0f7R/tx+3H7pPuk++D74Psc/Bz8UPxQ/H38ffyn/Kf81PzU/Aj9CP0//T/9c/1z/aH9of3M/cz99v32/Sn+Kf5m/mb+q/6r/vb+9v5C/0L/jv+O/9v/2/8pACkAeAB4AMQAxAAHAQcBQQFBAW0BbQGSAZIBtAG0AdkB2QEGAgYCOgI6AnACcAKjAqMC0wLTAvwC/AIhAyEDRQNFA2QDZAN8A3wDjAOMA5EDkQOPA48DjQONA4sDiwOKA4oDiAOIA4ADgANyA3IDZANkA1gDWANOA04DQwNDAy8DLwMPAw8D5QLlArgCuAKNAo0CaQJpAkgCSAIkAiQC/AH8AdMB0wGtAa0BkgGSAYABgAF0AXQBZwFnAVQBVAE9AT0BJQElAQ0BDQH3APcA3wDfAMYAxgCtAK0AlwCXAIYAhgB3AHcAZQBlAEsASwAmACYA+v/6/83/zf+g/6D/dP90/0f/R/8W/xb/4/7j/rP+s/6G/ob+Xf5d/jb+Nv4O/g7+5P3k/bz9vP2a/Zr9f/1//W79bv1h/WH9VP1U/Uf9R/05/Tn9K/0r/R/9H/0T/RP9Bv0G/fn8+fzu/O785fzl/OH84fzf/N/82vza/NT81PzM/Mz8w/zD/L/8v/y//L/8v/y//MH8wfy//L/8ufy5/LP8s/yu/K78rPys/K/8r/y1/LX8vvy+/Mv8y/zZ/Nn85/zn/PT89Pz//P/8Bf0F/Qn9Cf0L/Qv9DP0M/Q79Dv0S/RL9GP0Y/SL9Iv0v/S/9Qf1B/Vj9WP10/XT9lP2U/bb9tv3Y/dj99f31/Qz+DP4g/iD+MP4w/kT+RP5e/l7+fP58/p7+nv68/rz+0f7R/t3+3f7e/t7+1/7X/sr+yv62/rb+mP6Y/m7+bv41/jX+7f3t/Zr9mv0+/T792vza/HT8dPwJ/An8mfuZ+yn7Kfu7+rv6TPpM+uf55/mO+Y75QPlA+Qb5Bvnf+N/4yPjI+MX4xfjR+NH46fjp+BD5EPlD+UP5ffl9+cH5wfkM+gz6WPpY+qj6qPr5+vn6SftJ+5r7mvvr++v7Ofw5/Ib8hvzR/NH8F/0X/Vr9Wv2a/Zr91v3W/RD+EP5K/kr+hv6G/sn+yf4V/xX/bf9t/83/zf82ADYAoQChAAsBCwFzAXMB3QHdAUcCRwK1ArUCJgMmA5YDlgMFBAUEdQR1BOQE5ARYBVgF1AXUBVEGUQbRBtEGVAdUB9MH0wdWCFYI4wjjCHEJcQkDCgMKmgqaCicLJwuwC7ALOww7DL8MvwxBDUENxQ3FDTgOOA6fDp8O/g7+DkwPTA+OD44PzQ/ND/gP+A8PEA8QGBAYEAgQCBDrD+sPzA/MD6MPow90D3QPQw9DDwQPBA+6DroOcQ5xDiMOIw7ZDdkNnA2cDWINYg0tDS0NAQ0BDdMM0wykDKQMeQx5DEUMRQwHDAcMwwvDC3ELcQsSCxILrQqtCjsKOwq/Cb8JPgk+CbMIswggCCAIiAeIB+gG6AZABkAGlAWUBeIE4gQsBCwEdQN1A78CvwIKAgoCVwFXAakAqQD+//7/Vv9W/7D+sP4K/gr+Yv1i/br8uvwT/BP8bftt+8/6z/o1+jX6nvme+RD5EPmK+Ir4CvgK+Jr3mvc39zf32/bb9o32jfZJ9kn2C/YL9t313fW69br1m/Wb9Yj1iPV59Xn1afVp9WL1YvVi9WL1Y/Vj9Wz1bPV59Xn1gvWC9ZD1kPWf9Z/1qvWq9br1uvXK9cr11vXW9ef15/X79fv1DfYN9if2J/ZD9kP2XvZe9n/2f/ao9qj21vbW9hH3EfdZ91n3p/en9wL4Avhp+Gn42PjY+FL5UvnU+dT5VvpW+tf61/pV+1X70PvQ+0v8S/zG/Mb8P/0//bX9tf0n/if+lP6U/v/+//5p/2n/zv/O/y4ALgCIAIgA3wDfADQBNAGNAY0B5wHnAT0CPQKJAokCyQLJAvwC/AImAyYDTwNPA3cDdwOdA50DwgPCA98D3wP1A/UDCQQJBBcEFwQgBCAEJgQmBCUEJQQgBCAEHAQcBBkEGQQZBBkEHgQeBCAEIAQgBCAEIAQgBBwEHAQXBBcEEQQRBAQEBATvA+8D0gPSA60DrQOBA4EDTwNPAxYDFgPXAtcCkgKSAksCSwICAgICugG6AXABcAEiASIB0QDRAH4AfgAsACwA3v/e/5T/lP9I/0j/9/73/qH+of5H/kf+7v3u/Zv9m/1N/U39Af0B/bj8uPxw/HD8LPws/PT79PvG+8b7nvue+3v7e/tY+1j7MPsw+wz7DPvr+uv6y/rL+rD6sPqS+pL6cPpw+lD6UPo1+jX6H/of+hP6E/oM+gz6A/oD+vv5+/n3+ff59vn2+QL6AvoX+hf6Lfot+kP6Q/pX+lf6aPpo+n76fvqc+pz6vvq++ub65voQ+xD7OPs4+2T7ZPuW+5b7zvvO+w78DvxS/FL8lfyV/Nv82/wk/ST9cf1x/cL9wv0W/hb+aP5o/rf+t/4H/wf/XP9c/7n/uf8eAB4AhwCHAOsA6wBHAUcBmgGaAeQB5AEqAioCbQJtAqwCrALkAuQCFwMXA0UDRQNyA3IDogOiA9ED0QP+A/4DJgQmBEQERARaBFoEbARsBHoEegSGBIYEkQSRBJUElQSUBJQEkwSTBJMEkwSXBJcEowSjBK4ErgS4BLgEwgTCBMkEyQTTBNME5ATkBPYE9gQHBQcFEwUTBRUFFQUNBQ0FAwUDBfQE9ATjBOME0gTSBLwEvASjBKMEiwSLBHIEcgRXBFcEPQQ9BBwEHAT1A/UDygPKA5oDmgNoA2gDNwM3AwMDAwPLAssCkAKQAlQCVAIZAhkC4AHgAacBpwFtAW0BMQExAfIA8gCxALEAbgBuACwALADq/+r/qf+p/2v/a/8y/zL//P78/sn+yf6V/pX+Xv5e/iX+Jf7v/e/9vv2+/Zf9l/13/Xf9Wf1Z/Tr9Ov0Z/Rn99Pz0/NL80vyy/LL8lPyU/Hj8ePxe/F78RvxG/Db8Nvwr/Cv8I/wj/B38HfwV/BX8CvwK/AL8AvwA/AD8A/wD/Az8DPwV/BX8Gvwa/B78Hvwj/CP8K/wr/Dr8OvxO/E78Yfxh/HT8dPyF/IX8lPyU/KP8o/yw/LD8uvy6/ML8wvzJ/Mn8zPzM/M/8z/zP/M/8x/zH/Ln8ufyj/KP8hPyE/GP8Y/w8/Dz8D/wP/N/73/up+6n7bPts+y37Lfvp+un6nPqc+kv6S/r0+fT5mfmZ+UX5Rfn2+Pb4q/ir+Gj4aPgq+Cr48ffx98r3yve497j3uPe498z3zPfs9+z3DvgO+Dj4OPhr+Gv4pvim+PD48PhD+UP5mPmY+e/57/lI+kj6pPqk+gf7B/tw+3D72PvY+zz8PPyb/Jv89vz2/FP9U/2z/bP9Ff4V/nT+dP7P/s/+Jv8m/37/fv/c/9z/QgBCAK0ArQAbARsBigGKAfoB+gFuAm4C6gLqAmsDawPsA+wDawRrBOAE4ARPBU8FvwW/BS8GLwajBqMGHgceB5YHlgcOCA4IiQiJCAIJAgl9CX0J/gn+CXoKegr0CvQKcQtxC+YL5gtZDFkM0AzQDDwNPA2iDaINCA4IDmEOYQ6zDrMOBg8GD00PTQ+KD4oPwg/CD+YP5g/5D/kPBBAEEPoP+g/hD+EPwg/CD5QPlA9cD1wPIg8iD9oO2g6LDosOOw47DuUN5Q2NDY0NPQ09DesM6wyaDJoMUQxRDAQMBAy1C7ULaQtpCxULFQu7CrsKXwpfCvoJ+gmPCY8JIwkjCa4IrggyCDIIsQexBykHKQecBpwGEgYSBocFhwX5BPkEaQRpBNED0QMyAzIDjwKPAu0B7QFNAU0BsQCxABoAGgCF/4X/8v7y/mL+Yv7V/dX9Sv1K/cT8xPxB/EH8vfu9+z/7P/vF+sX6S/pL+tf51/ll+WX58vjy+Ij4iPgm+Cb4yvfK9333ffc79zv3/Pb89sj2yPad9p32dfZ19lv2W/ZJ9kn2O/Y79jX2NfY09jT2NfY19j72PvZO9k72YPZg9nz2fPae9p72wvbC9u727vYd9x33RvdG93D3cPeX95f3ufe599/33/cJ+An4Nfg1+Gf4Z/id+J341PjU+BL5EvlW+Vb5nvme+e757vlC+kL6l/qX+u767vpH+0f7ofuh+/37/ftc/Fz8u/y7/Bn9Gf10/XT9zf3N/ST+JP59/n3+2P7Y/jT/NP+Q/5D/6P/o/zsAOwCKAIoA2ADYACcBJwF3AXcBxwHHARICEgJVAlUCkQKRAsYCxgL3AvcCKAMoA1MDUwN5A3kDmQOZA7IDsgPHA8cD3APcA+4D7gP7A/sDBAQEBAEEAQT1A/UD4wPjA8wDzAOzA7MDmgOaA34DfgNfA18DPgM+AxgDGAPuAu4CwQLBApACkAJbAlsCIgIiAuQB5AGhAaEBWgFaAQ8BDwHAAMAAbQBtABgAGADB/8H/aP9o/w//D/+2/rb+X/5f/gz+DP69/b39b/1v/SX9Jf3b/Nv8lPyU/FX8Vfwe/B787fvt+8H7wfuY+5j7bvtu+0n7Sfsp+yn7DvsO+/j6+Prj+uP6yfrJ+qz6rPqN+o36avpq+kr6Svor+iv6B/oH+uD54Pmz+bP5gPmA+VD5UPkk+ST5+fj5+NX41fiy+LL4jPiM+Gz4bPhS+FL4Pvg++Df4N/g5+Dn4Pvg++Er4Svhb+Fv4bPhs+If4h/iq+Kr40PjQ+P34/fgt+S35Xvle+Zj5mPnc+dz5Kfop+n/6f/rZ+tn6Mfsx+4n7ifvj++P7QvxC/Kn8qfwY/Rj9hv2G/fD98P1X/lf+vf69/ib/Jv+X/5f/CwALAHsAewDkAOQARAFEAZ8BnwH7AfsBWwJbArsCuwIVAxUDZQNlA6gDqAPjA+MDIAQgBF0EXQSZBJkE1ATUBAMFAwUpBSkFTAVMBW4FbgWTBZMFvQW9BeUF5QUKBgoGMAYwBlMGUwZ5BnkGpQalBs4Gzgb1BvUGGgcaBzcHNwdPB08HaQdpB38HfweRB5EHogeiB6sHqweqB6oHpAekB5MHkwd5B3kHXQddBzsHOwcYBxgH9gb2BtEG0QapBqkGgQaBBlYGVgYrBisGBAYEBt4F3gW3BbcFkgWSBWgFaAU8BTwFEgUSBeYE5gS6BLoEjgSOBF4EXgQtBC0E/AP8A8oDygOXA5cDYQNhAyQDJAPgAuACmAKYAk4CTgIEAgQCuwG7AXIBcgElASUB1ADUAIMAgwAyADIA5f/l/5v/m/9T/1P/DP8M/8X+xf5//n/+Of45/vb99v2z/bP9bv1u/Sv9K/3q/Or8qvyq/G/8b/w4/Dj8AvwC/ND70Pui+6L7dvt2+1L7Uvs1+zX7Gvsa+wb7Bvv2+vb66vrq+uX65fro+uj67/rv+v76/voS+xL7KPso+0P7Q/tg+2D7fPt8+5n7mfu1+7X7zPvM++P74/v4+/j7BfwF/A78DvwR/BH8DPwM/AT8BPz7+/v77Pvs+9r72vvC+8L7o/uj+4P7g/tg+2D7Ovs6+xP7E/vn+uf6tPq0+n/6f/pJ+kn6EPoQ+tr52vmj+aP5ZPlk+ST5JPnl+OX4pfil+G/4b/hB+EH4FvgW+PP38/fW99b3vfe996/3r/eq96r3q/er97X3tffH98f34Pfg9wj4CPg7+Dv4dvh2+Lr4uvgD+QP5TvlO+aH5ofn5+fn5VPpU+rD6sPoN+w37Z/tn+8P7w/sj/CP8hvyG/Oj86PxI/Uj9o/2j/f39/f1Z/ln+u/67/iP/I/+O/47/+//7/2YAZgDUANQARgFGAbwBvAE2AjYCswKzAiwDLAOmA6YDIwQjBKMEowQnBScFsAWwBTUGNQa4BrgGPwc/B8IHwgdJCEkI1QjVCFsJWwnfCd8JZQplCuQK5ApjC2ML5QvlC10MXQzPDM8MQg1CDaoNqg0QDhAOeQ55DtYO1g4oDygPcw9zD6wPrA/aD9oPBRAFEB8QHxAsECwQMRAxECIQIhAJEAkQ8Q/xD88Pzw+mD6YPew97D0APQA/8DvwOuw67DnMOcw4qDioO5A3kDZQNlA08DTwN5QzlDIkMiQwrDCsM0QvRC3ALcAsJCwkLowqjCjYKNgrICcgJWwlbCecI5whsCGwI7gfuB2sHawfkBuQGXgZeBtUF1QVGBUYFtQS1BCAEIASLA4sD/AL8AnMCcwLsAewBZgFmAd8A3wBXAFcA0P/Q/0v/S//J/sn+SP5I/sn9yf1K/Ur9yvzK/E/8T/zX+9f7Xvte++v66/p4+nj6BfoF+pf5l/kv+S/5yfjJ+G/4b/gc+Bz4zvfO94r3ivdN9033EvcS9+L24va89rz2m/ab9ob2hvZ39nf2afZp9mP2Y/Zh9mH2YvZi9m32bfZ99n32ivaK9pn2mfao9qj2s/az9sb2xvbf9t/2+/b79h/3H/dE90T3Zvdm9433jfe597n35/fn9x34HfhY+Fj4kviS+NH40fgV+RX5Wvla+aT5pPnz+fP5QvpC+pT6lPro+uj6Pfs9+5b7lvvw+/D7SPxI/KD8oPz4/Pj8Tv1O/aX9pf36/fr9Tv5O/qD+oP7w/vD+Pv8+/4r/iv/T/9P/GAAYAFkAWQCYAJgA1wDXABYBFgFVAVUBkgGSAckByQH5AfkBJAIkAkoCSgJrAmsCigKKAqMCowK0ArQCvgK+AsECwQK/Ar8CuwK7ArMCswKoAqgCnAKcAowCjAJ5AnkCYwJjAkgCSAIoAigCBAIEAt0B3QGzAbMBhwGHAVoBWgEnAScB8ADwALUAtQB3AHcANwA3APT/9P+t/63/YP9g/w3/Df+1/rX+W/5b/gH+Af6m/ab9Sf1J/e/87/yY/Jj8R/xH/AD8APzC+8L7iPuI+1T7VPsj+yP79vr2+tP60/q5+rn6ovqi+o/6j/p6+nr6Yfph+kv6S/o8+jz6Mfox+i36Lfop+in6H/of+hT6FPoK+gr6AfoB+v75/vn7+fv58vny+ef55/nZ+dn5y/nL+cP5w/nC+cL5wPnA+cD5wPm++b75uPm4+bX5tfm3+bf5uvm6+cD5wPnF+cX5xvnG+cv5y/nV+dX55Pnk+f35/fkb+hv6O/o7+mH6YfqN+o36wPrA+v/6//pM+0z7oPug+/77/vti/GL8yvzK/Df9N/2q/ar9Iv4i/p7+nv4b/xv/mf+Z/xYAFgCSAJIADgEOAYoBigEFAgUCfAJ8AusC6wJRA1EDsQOxAwoECgRhBGEEuQS5BAoFCgVWBVYFnAWcBdgF2AURBhEGTQZNBokGiQbJBskGCwcLB0gHSAd/B38Htwe3B+gH6AcWCBYIRQhFCGoIagiHCIcIogiiCLQItAjFCMUI2gjaCOsI6wj5CPkIBwkHCQ8JDwkUCRQJHQkdCSQJJAkqCSoJMwkzCTYJNgk2CTYJNwk3CTIJMgkrCSsJJAkkCRUJFQn/CP8I5wjnCMUIxQicCJwIcAhwCDwIPAgCCAIIxgfGB4EHgQc2BzYH6QbpBpUGlQY+Bj4G6gXqBZUFlQVBBUEF7gTuBJoEmgRGBEYE8wPzA6MDowNUA1QDCAMIA7kCuQJnAmcCEAIQArcBtwFbAVsB/wD/AKEAoQBAAEAA3P/c/3X/df8N/w3/o/6j/jv+O/7S/dL9Z/1n/f38/fyV/JX8MPww/NT71Pt9+337J/sn+9X61fqG+ob6Ofo5+vb59vm8+bz5hvmG+Vr5Wvkx+TH5CPkI+eb45vjK+Mr4sviy+KL4oviY+Jj4kPiQ+I/4j/iX+Jf4o/ij+Lr4uvjY+Nj4+fj5+B75HvlH+Uf5b/lv+Z35nfnP+c/5AvoC+jr6Ovpy+nL6pvqm+tj62PoG+wb7Lfst+1H7Uftw+3D7iPuI+5z7nPuq+6r7rvuu+637rfum+6b7lfuV+4D7gPtl+2X7QvtC+xz7HPv1+vX6yvrK+p/6n/pz+nP6QPpA+gz6DPrV+dX5mfmZ+WH5Yfkt+S35+vj6+M34zfik+KT4e/h7+Fn4Wfg9+D34Jfgl+Br4GvgY+Bj4GfgZ+CX4Jfg3+Df4TfhN+HD4cPie+J741fjV+Bn5Gflk+WT5s/mz+Qj6CPpk+mT6xPrE+i37Lfua+5r7BvwG/HH8cfzb/Nv8Rf1F/bH9sf0g/iD+kf6R/v7+/v5o/2j/zv/O/zQANACfAJ8ADwEPAYEBgQHyAfIBYQJhAs0CzQI8AzwDsgOyAywELASoBKgEJgUmBZ4FngUWBhYGlQaVBhgHGAeiB6IHNAg0CL8IvwhICUgJ1AnUCVwKXArpCukKfAt8CwcMBwyLDIsMDA0MDYQNhA37DfsNeA54Du0O7Q5ZD1kPvw+/DxEQERBYEFgQnRCdENMQ0xD9EP0QIBEgESwRLBElESURFxEXEfcQ9xDMEMwQmxCbEFgQWBAIEAgQtA+0D1YPVg/xDvEOkA6QDiUOJQ6zDbMNQg1CDcgMyAxMDEwM1QvVC1kLWQvbCtsKYApgCuEJ4QlhCWEJ5gjmCGwIbAj0B/QHgQeBBw0HDQeZBpkGJgYmBq8FrwU2BTYFuwS7BD4EPgTAA8ADQgNCA8ICwgI/Aj8CuQG5AS8BLwGiAKIAFAAUAIX/hf/1/vX+Yv5i/s/9z/05/Tn9ofyh/Az8DPx3+3f74vri+lH6UfrC+cL5NPk0+a/4r/gy+DL4uve69073Tvfr9uv2ivaK9jX2Nfbn9ef1nvWe9WP1Y/Uy9TL1BfUF9eX05fTO9M70u/S79LX0tfS59Ln0w/TD9Nr02vT59Pn0GfUZ9UD1QPVp9Wn1kPWQ9bz1vPXq9er1FvYW9kX2RfZ19nX2o/aj9tX21fYM9wz3RfdF94T3hPfH98f3CfgJ+E74TviW+Jb43/jf+DD5MPmF+YX53fnd+Tj6OPqU+pT67/rv+k37Tfuv+6/7EvwS/Hr8evzj/OP8TP1M/bX9tf0d/h3+hv6G/u7+7v5Y/1j/wf/B/yYAJgCHAIcA5gDmAEIBQgGcAZwB9QH1AUoCSgKZApkC4gLiAiMDIwNgA2ADmgOaA88DzwP8A/wDIQQhBDkEOQRHBEcEUARQBFQEVARUBFQEUARQBEIEQgQqBCoEDQQNBOoD6gPHA8cDpgOmA4EDgQNWA1YDKAMoA/UC9QLCAsICkQKRAmACYAIsAiwC8QHxAbABsAFtAW0BLAEsAfAA8AC1ALUAdwB3ADIAMgDl/+X/kv+S/z7/Pv/r/uv+l/6X/j/+P/7g/eD9fP18/Rr9Gv29/L38Zfxl/BT8FPzE+8T7dPt0+yj7KPvk+uT6qPqo+nn6efpT+lP6L/ov+g36Dfrt+e35zfnN+bT5tPmj+aP5lPmU+Yv5i/mF+YX5ffl9+Xf5d/lz+XP5bflt+Wv5a/lq+Wr5Zfll+WH5Yfld+V35VvlW+VT5VPlY+Vj5Xvle+Wr5avl2+Xb5fvl++Yf5h/mT+ZP5ovmi+br5uvnX+df59Pn0+RP6E/oz+jP6U/pT+nz6fPqu+q766Pro+in7Kfty+3L7wfvB+xv8G/yB/IH87/zv/GT9ZP3c/dz9Vf5V/s/+z/5N/03/0P/Q/1QAVADXANcAVgFWAdEB0QFLAksCxwLHAj8DPwOzA7MDIQQhBIMEgwTeBN4EOAU4BY8FjwXlBeUFOgY6BoYGhgbNBs0GEwcTB1UHVQeXB5cH2gfaBxYIFghNCE0IggiCCLAIsAjbCNsIBgkGCSYJJgk+CT4JVAlUCWMJYwlwCXAJggmCCY8JjwmZCZkJowmjCaUJpQmmCaYJqgmqCasJqwmrCasJrQmtCakJqQmiCaIJnQmdCZAJkAl/CX8JbAlsCVAJUAksCSwJBgkGCdYI1gibCJsIWAhYCAcIBwisB6wHTQdNB+cG5wZ8BnwGEAYQBp8FnwUsBSwFugS6BEsESwTeA94DeAN4AxUDFQO2ArYCXAJcAgcCBwK0AbQBYwFjARIBEgHAAMAAbwBvACEAIQDU/9T/hv+G/zT/NP/c/tz+fv5+/h3+Hf65/bn9Uv1S/ev86/yB/IH8FfwV/K77rvtM+0z77/rv+pz6nPpN+k36//n/+bj5uPl4+Xj5Pfk9+Q75Dvnl+OX4vfi9+Jn4mfh5+Hn4W/hb+EX4Rfg1+DX4JPgk+Bj4GPgM+Az4AfgB+AD4APgI+Aj4F/gX+DH4MfhT+FP4evh6+Kr4qvjj+OP4JPkk+W/5b/nE+cT5H/of+n/6f/rk+uT6RvtG+6f7p/sG/Ab8Xvxe/LP8s/wC/QL9Rv1G/X79fv2o/aj9w/3D/dT91P3c/dz92/3b/dH90f28/bz9m/2b/XT9dP1L/Uv9H/0f/fP88/zB/MH8hvyG/EX8RfwB/AH8vfu9+3/7f/tD+0P7A/sD+8D6wPp4+nj6K/or+uP54/mj+aP5ZPlk+Sr5Kvnx+PH4t/i3+IX4hfhg+GD4RPhE+Df4N/gz+DP4Mvgy+Dr4OvhN+E34aPho+JP4k/jJ+Mn4AvkC+UL5QvmH+Yf50PnQ+SD6IPp3+nf6zPrM+h/7H/tu+277t/u3+wH8AfxO/E78nfyd/O/87/xB/UH9kf2R/eL94v03/jf+k/6T/vX+9f5g/2D/0v/S/0cARwDEAMQASQFJAdYB1gFtAm0CDAMMA64DrgNUBFQEAAUABa0FrQVgBmAGHAccB9QH1AeLCIsIQglCCfAJ8AmaCpoKSAtIC+4L7guQDJAMMQ0xDcINwg1KDkoOzg7ODkUPRQ+zD7MPIBAgEHwQfBDLEMsQFREVEU0RTRF6EXoRohGiEbYRthG6EboRtRG1EZsRmxFzEXMRSRFJERERERHRENEQjxCPED8QPxDnD+cPkA+QDzAPMA/ODs4OcA5wDg0ODQ6pDakNSg1KDekM6QyJDIkMLwwvDNEL0QtvC28LDgsOC6UKpQo2CjYKyAnICVUJVQndCN0IZQhlCOcH5wdhB2EH1wbXBkUGRQapBakFCAUIBWAEYASyA7IDAgMCA1ACUAKdAZ0B6ADoADUANQCD/4P/0v7S/ib+Jv5+/X791/zX/Db8NvyX+5f7+vr6+mb6Zvra+dr5U/lT+dr42vhp+Gn4+/f795f3l/c59zn33fbd9oz2jPZF9kX2APYA9sX1xfWO9Y71V/VX9Sj1KPUA9QD12vTa9L/0v/Sr9Kv0l/SX9I30jfSK9Ir0ifSJ9JL0kvSi9KL0sPSw9Mb0xvTg9OD0+PT49BX1FfU29Tb1U/VT9Xb1dvWf9Z/1yvXK9QD2APY+9j72f/Z/9sj2yPYX9xf3afdp98X3xfcs+Cz4mfiZ+A/5D/mN+Y35DfoN+pL6kvoc+xz7qfup+zn8OfzL/Mv8Xf1d/ez97P15/nn+AP8A/3//f//0//T/XwBfAL8AvwAXARcBZwFnAbABsAH0AfQBNAI0Am0CbQKiAqIC1QLVAgQDBAMwAzADWwNbA4ADgAOkA6QDyAPIA+oD6gMJBAkEJgQmBDwEPARNBE0EWwRbBGQEZARqBGoEbgRuBGwEbARlBGUEWwRbBE0ETQQ7BDsEJgQmBAsECwTpA+kDwwPDA5kDmQNtA20DRQNFAxwDHAPxAvECwQLBAokCiQJJAkkCAwIDArcBtwFjAWMBBgEGAZ4AngApACkAqP+o/yH/If+V/pX+Bv4G/nb9dv3k/OT8TvxO/Lv7u/sv+y/7qvqq+jX6NfrN+c35bvlu+Rz5HPnY+Nj4oPig+Hz4fPhp+Gn4Yfhh+GT4ZPhs+Gz4dfh1+IT4hPiY+Jj4rviu+Mj4yPjg+OD47/jv+Pv4+/gF+QX5C/kL+RX5Ffkg+SD5J/kn+Sz5LPkw+TD5Mvky+T35PflS+VL5b/lv+ZT5lPm7+bv53/nf+QP6A/oq+ir6U/pT+oL6gvq0+rT64/rj+g/7D/s3+zf7XPtc+4T7hPuw+7D74Pvg+xb8FvxR/FH8j/yP/NX81fwo/Sj9h/2H/fT99P1s/mz+7f7t/nX/df8FAAUAngCeADwBPAHeAd4BfQJ9AhMDEwOiA6IDLAQsBK4ErgQrBSsFogWiBQgGCAZeBl4GpgamBuAG4AYUBxQHSQdJB3oHegepB6kH2QfZBwUIBQgzCDMIawhrCKcIpwjmCOYIKQkpCWMJYwmaCZoJ0gnSCQYKBgo4CjgKawprCo8KjwqmCqYKsgqyCq4KrgqfCp8KjAqMCmwKbApCCkIKFAoUCtwJ3AmhCaEJaQlpCS0JLQnuCO4IrwivCGoIaggiCCII3wffB5wHnAdbB1sHGwcbB9MG0waDBoMGLwYvBtQF1AV0BXQFDwUPBaMEowQtBC0EsQOxAzIDMgO1ArUCPwI/As4BzgFjAWMB+gD6AJUAlQA3ADcA5P/k/57/nv9m/2b/Nv82/wv/C//j/uP+vP68/pj+mP51/nX+T/5P/iT+JP7u/e79rP2s/WL9Yv0S/RL9u/y7/F78Xvz3+/f7hfuF+xH7Efue+p76L/ov+sz5zPlx+XH5F/kX+cX4xfh8+Hz4Pfg9+BT4FPj+9/738ffx9+737vfu9+737/fv9/r3+vcR+BH4Lvgu+FX4Vfh++H74pPik+M34zfj8+Pz4L/kv+W35bfmy+bL5+vn6+Un6Sfqi+qL6AvsC+277bvvh++H7VfxV/Mn8yfw9/T39rv2u/R7+Hv6K/or+7v7u/kT/RP+L/4v/wP/A/+b/5v/7//v/AAAAAPP/8//U/9T/pf+l/2r/av8n/yf/3P7c/o7+jv46/jr+4f3h/Yn9if0z/TP94fzh/JX8lfxM/Ez8AvwC/Lj7uPtv+2/7JPsk+9763vqX+pf6R/pH+vD58PmQ+ZD5KPko+cT4xPhm+Gb4CfgJ+LL3svdc91z3AvcC97H2sfZr9mv2MfYx9gz2DPb29fb15vXm9eP14/Xs9ez1APYA9ij2KPZi9mL2pPak9u327fY59zn3g/eD99T31Pcq+Cr4g/iD+OD44Pg7+Tv5kfmR+ef55/lB+kH6ofqh+g37DfuE+4T7A/wD/Ir8ivwe/R79wP3A/XD+cP4x/zH//////9UA1QCzAbMBmAKYAoADgANtBG0EYgViBVMGUwZCB0IHMAgwCBMJEwnvCe8JywrLCpoLmgtgDGAMIw0jDdMN0w14DngOGw8bD64Prg88EDwQzxDPEFYRVhHZEdkRXxJfEtUS1RJFE0UTtxO3ExsUGxR3FHcU1BTUFBsVGxVSFVIVfxV/FZMVkxWVFZUVkhWSFXoVehVTFVMVJRUlFeEU4RSPFI8UOxQ7FNkT2RNvE28TBxMHE5ESkRIREhESkxGTEQsRCxF/EH8Q+g/6D20PbQ/bDtsOSA5IDq0NrQ0KDQoNZwxnDLsLuwsGCwYLSgpKCoAJgAmrCKsI0QfRB+8G7wYGBgYGGAUYBSUEJQQuAy4DOQI5AksBSwFjAGMAgf+B/6b+pv7P/c/9/Pz8/DL8Mvxz+3P7vvq++hX6Ffp1+XX51vjW+EH4Qfi097T3K/cr9632rfY29jb2vPW89Uj1SPXW9Nb0YvRi9Pjz+POS85LzKfMp88fyx/Jq8mryDfIN8sDxwPF+8X7xP/E/8Q/xD/Hm8ObwwfDB8K7wrvCq8KrwrPCs8L/wv/Db8Nvw9/D38B/xH/FR8VHxhvGG8cjxyPEQ8hDyVvJW8qTypPL58vnyUfNR87bztvMl9CX0l/SX9BL1EvWX9Zf1IfYh9rr2uvZd9133BvgG+Lf4t/ht+W35J/on+un66fqz+7P7gPyA/E39Tf0V/hX+1f7V/ov/i/85ADkA4ADgAH0BfQERAhECmQKZAhEDEQN8A3wD3wPfAzcENwSIBIgE2ATYBCAFIAVlBWUFqAWoBeUF5QUjBiMGYwZjBqEGoQbeBt4GGwcbB1AHUAd/B38HrQetB9UH1Qf5B/kHGQgZCCwILAgxCDEILAgsCBkIGQj7B/sH2AfYB6cHpwdpB2kHIQchB8wGzAZxBnEGFgYWBrgFuAVWBVYF8QTxBIMEgwQQBBAEnAOcAycDJwOwArACNQI1ArABsAEgASABhgCGAOj/6P9F/0X/nf6d/vD98P05/Tn9dvx2/LL7svvt+u36LPos+nb5dvnJ+Mn4Ifgh+In3iff/9v/2g/aD9h/2H/bO9c71i/WL9Vz1XPU+9T71KfUp9Sf1J/Uv9S/1N/U39UT1RPVP9U/1VfVV9WD1YPVt9W31dvV29X/1f/WB9YH1d/V39W/1b/Vr9Wv1a/Vr9Xj1ePWL9Yv1nPWc9bP1s/XU9dT1AvYC9kr2Svap9qn2EvcS94L3gvfx9/H3Xvhe+NP40/hU+VT52/nb+Wb6Zvrq+ur6Yfth+9H70ftB/EH8uPy4/DT9NP2x/bH9Jv4m/pP+k/4C/wL/e/97/wUABQCdAJ0AOQE5AcwBzAFYAlgC4gLiAnIDcgMMBAwErwSvBEkFSQXSBdIFSwZLBrUGtQYbBxsHhgeGB+gH6Ac8CDwIfwh/CKwIrAjPCM8I+gj6CCkJKQleCV4JlgmWCcUJxQnwCfAJJAokCmAKYAqmCqYK9gr2Cj4LPgt6C3oLsguyC94L3gsEDAQMKQwpDD0MPQw7DDsMJwwnDPsL+wvAC8ALfgt+Cy4LLgvPCs8KZQplCuwJ7AlqCWoJ6gjqCGgIaAjnB+cHaAdoB+YG5gZmBmYG7gXuBX4FfgUYBRgFugS6BF4EXgT/A/8DoAOgA0ADQAPgAuACggKCAiMCIwLAAcABWAFYAe0A7QCBAIEAGQAZALj/uP9f/1//Df8N/8L+wv5+/n7+Q/5D/hP+E/7t/e39zv3O/bP9s/2Y/Zj9fP18/WH9Yf1I/Uj9Lv0u/RH9Ef3t/O38vPy8/IP8g/xC/EL8+vv6+7L7svtm+2b7EvsS+776vvpp+mn6GfoZ+tf51/mm+ab5gfmB+W/5b/lt+W35ePl4+Zb5lvnE+cT5+vn6+Tr6Ovp++n76wvrC+gr7CvtV+1X7nvue++X75fsj/CP8VvxW/IP8g/yt/K381/zX/AP9A/0v/S/9Wf1Z/YL9gv2u/a794P3g/Rz+HP5j/mP+sv6y/gb/Bv9f/1//u/+7/xkAGQB5AHkA1gDWACsBKwF0AXQBrgGuAdUB1QHnAecB5QHlAdAB0AGnAacBbgFuAScBJwHUANQAdwB3ABMAEwCr/6v/Q/9D/+D+4P6A/oD+I/4j/sb9xv1o/Wj9Bv0G/af8p/xI/Ej85vvm+4H7gfsQ+xD7jfqN+vz5/Pld+V35sviy+AT4BPhN9033h/aH9r31vfXv9O/0IPQg9GbzZvPB8sHyLfIt8rPxs/FP8U/x/fD98NDw0PDK8Mrw4/Dj8B/xH/Fx8XHxyfHJ8TDyMPKk8qTyJPMk87XztfNN9E302fTZ9Fj1WPXG9cb1JfYl9of2h/by9vL2Yfdh99H30fc7+Dv4n/if+Aj5CPmG+Yb5H/of+tP60/qc+5z7cfxx/E79Tv08/jz+Qv9C/2EAYQCYAZgB3ALcAhoEGgRTBVMFiwaLBr8Hvwf3CPcIMwozCl0LXQt0DHQMfg1+DXQOdA5nD2cPZBBkEFkRWRFIEkgSNBM0EwwUDBTdFN0UuRW5FY8WjxZjF2MXORg5GPIY8hiWGZYZMhoyGrQatBolGyUbjhuOG9Ab0BvuG+4b9Rv1G9Qb1BuZG5kbVBtUG/Ia8hp4Gnga8RnxGU4ZThmaGJoY6hfqFzEXMRd5FnkWyhXKFRQVFBVcFFwUsBOwEwMTAxNbElsSvBG8ERURFRFlEGUQrg+uD+cO5w4RDhEONA00DUYMRgxGC0YLNgo2ChEJEQnaB9oHmAaYBk4FTgX8A/wDqAKoAlQBVAEBAAEAtP60/nX9df1D/EP8Hvse+wv6C/oG+Qb5C/gL+CT3JPdN9k32gPWA9cb0xvQU9BT0YvNi87zyvPIb8hvyefF58eHw4fBL8Evwre+t7xbvFu+D7oPu7+3v7W/tb+3+7P7skeyR7DnsOezw6/Drs+uz65XrleuT65Proeuh68nryesA7ADsOOw47ILsguzZ7NnsOO047antqe0i7iLule6V7gzvDO+D74Pv+e/5733wffAO8Q7xovGi8T/yP/Lh8uHyhPOE8zX0NfT49Pj0zPXM9bT2tPaq96r3pvim+Kv5q/m7+rv61fvV+/n8+fwh/iH+Rv9G/1wAXABkAWQBXQJdAkYDRgMjBCME8QTxBKYFpgVDBkMGygbKBjsHOwegB6AHAwgDCF0IXQiwCLAI/gj+CEAJQAl9CX0JwAnACQQKBApLCksKlwqXCtkK2QoTCxMLSgtKC3YLdgudC50LwwvDC9oL2gvjC+ML4AvgC8cLxwuaC5oLYAtgCxILEguxCrEKQgpCCsEJwQkxCTEJmwibCPsH+wdUB1QHqwarBvwF/AVJBUkFlQSVBN8D3wMnAycDbwJvArcBtwH+AP4AQgBCAIb/hv/D/sP+9/33/Sb9Jv1Q/FD8dPt0+5v6m/rC+cL55Pjk+An4Cfgr9yv3SPZI9m/1b/Wd9J30zPPM8wnzCfNN8k3ykvGS8erw6vBR8FHwwu/C703vTe/r7uvule6V7lnuWe407jTuG+4b7hzuHO4r7ivuPe497l/uX+6M7ozuv+6/7gPvA+9S71Lvne+d7+zv7O878DvwhvCG8OHw4fBL8Uvxu/G78TbyNvK18rXyL/Mv87Pzs/NF9EX05fTl9Jb1lvVU9lT2FPcU99r32ver+Kv4ifmJ+Xv6e/p8+3z7g/yD/Ib9hv2D/oP+e/97/3IAcgBtAW0BawJrAl0DXQM9BD0ECwULBccFxwV/Bn8GPgc+B/4H/ge9CL0Idwl3CR8KHwq9Cr0KXgteC/8L/wujDKMMSQ1JDdsN2w1ZDlkOyw7LDi0PLQ+KD4oP7A/sD0AQQBCBEIEQsxCzEMoQyhDRENEQ1hDWEM0QzRC2ELYQkBCQEEwQTBD0D/QPmA+YDzUPNQ/TDtMOdw53DhEOEQ6hDaENMQ0xDb0MvQxMDEwM5QvlC3sLewsKCwoLkwqTChAKEAqICYgJAAkACXUIdQjiB+IHSQdJB6YGpgb9Bf0FWAVYBbUEtQQUBBQEcgNyA8sCywIeAh4CcQFxAccAxwAjACMAgv+C/+X+5f5J/kn+rv2u/R39Hf2Z/Jn8IPwg/LX7tftR+1H78frx+pn6mfpL+kv6BPoE+sr5yvmY+Zj5Zvlm+Tv5O/kX+Rf5+vj6+O347fju+O749fj1+Ab5Bvkf+R/5PPk8+WT5ZPmU+ZT5x/nH+f35/fkx+jH6Yfph+pD6kPrB+sH68vry+ij7KPti+2L7mvua+9T71PsP/A/8SvxK/Ib8hvzF/MX8Bf0F/Ub9Rv2J/Yn9y/3L/Q/+D/5U/lT+nP6c/ur+6v4//z//mv+a//r/+v9dAF0AvwC/ACABIAF9AX0B1wHXASoCKgJzAnMCswKzAuYC5gIPAw8DMwMzA04DTgNlA2UDeAN4A4cDhwOUA5QDpAOkA7QDtAPDA8MD0QPRA9oD2gPfA98D5APkA+oD6gPvA+8D8wPzA+0D7QPbA9sDvwO/A5oDmgNrA2sDMgMyA+kC6QKKAooCEwITAooBigHzAPMAVQBVALP/s/8I/wj/U/5T/pr9mv3i/OL8Mfwx/JL7kvsA+wD7b/pv+uD54PlP+U/5vvi++Dj4OPi597n3Nvc296n2qfYE9gT2QvVC9XT0dPSe857zwfLB8uXx5fH/8P/wAfAB8APvA+8L7gvuIe0h7V7sXuy767vrKOso66/qr+pN6k3qBOoE6u/p7+kO6g7qUupS6r7qvupB60HryuvK62rsauwi7SLt6u3q7cXuxe6j76PvcPBw8DDxMPHl8eXxkfKR8kTzRPP+8/7zt/S39HT1dPU29jb2AfcB9+T35Pfr+Ov4FfoV+mD7YPvG/Mb8Q/5D/s//z/9xAXEBLAMsA/QE9ATIBsgGoAigCGcKZwojDCMM2A3YDXoPeg8RERERohKiEhQUFBRrFWsVsRaxFtEX0RfdGN0Y5RnlGdUa1Rq3G7cbmhyaHGQdZB0jHiMe6h7qHp8fnx9MIEwg/CD8II4hjiEHIgcidiJ2Ir0ivSLkIuQi+SL5It4i3iKcIpwiQiJCIrwhvCEVIRUhYCBgIIsfix+gHqAesx2zHbEcsRynG6cbpBqkGpYZlhmEGIQYeRd5F2UWZRZNFU0VOhQ6FCATIBP9Ef0R1hDWEJ8Pnw9YDlgOCQ0JDaoLqgs5CjkKugi6CCgHKAd/BX8FxQPFAwICAgI4ADgAa/5r/qf8p/zp+un6M/kz+ZX3lfcO9g72n/Sf9FXzVfMp8inyD/EP8RPwE/Aq7yrvS+5L7oXthe3N7M3sFewV7Gvra+vC6sLqEOoQ6m3pbenT6NPoOOg46LHnsecw5zDnquaq5jXmNebQ5dDldeV15T3lPeUi5SLlFuUW5S3lLeVg5WDlpuWm5RXmFeap5qnmU+dT5yDoIOgE6QTp7unu6erq6ury6/Lr+uz67AruCu4e7x7vKvAq8DjxOPFG8kbyT/NP81z0XPRv9W/1g/aD9p73nvfD+MP48Pnw+ST7JPtg/GD8of2h/eL+4v4kACQAZwFnAaQCpALeA94DEwUTBTkGOQZSB1IHYAhgCFgJWAk/Cj8KGAsYC9kL2QuIDIgMKg0qDbQNtA0vDi8Oog6iDgQPBA9dD10Ptw+3DwgQCBBVEFUQpRClEOkQ6RAmESYRYhFiEY8RjxGyEbIRzhHOEdIR0hG8EbwRkxGTEUgRSBHeEN4QXxBfEMEPwQ8GDwYPOQ45DlUNVQ1gDGAMZwtnC2cKZwpkCWQJYAhgCFoHWgdTBlMGTwVPBVAEUARUA1QDXAJcAmUBZQFsAGwAcP9w/3f+d/5+/X79hPyE/I37jfuQ+pD6i/mL+YT4hPh493j3ZvZm9lr1WvVQ9FD0Q/ND80PyQ/JK8UrxVvBW8HjveO+u7q7u7+3v7U7tTu3A7MDsOuw67Mvry+to62jrBusG67Xqtepv6m/qJeol6urp6um16bXpf+l/6V7pXulV6VXpYOlg6ZXplent6e3pXOpc6u3q7eqa65rrWuxa7DvtO+047jjuRO9E72HwYfCF8YXxo/Kj8sLzwvPh9OH0/PX89RX3Ffco+Cj4Lfkt+ST6JPoM+wz75fvl+7T8tPx+/X79RP5E/gr/Cv/V/9X/qgCqAIkBiQF8AnwChgOGA6QEpATYBdgFIgciB3IIcgjJCckJIwsjC3AMcAyvDa8N3g7eDukP6Q/QENAQlhGWETASMBKoEqgSDBMME04TThN2E3YTixOLE38TfxNfE18TOxM7Ew0TDRPdEt0SshKyEnkSeRI3EjcS9RH1EawRrBFkEWQRJhEmEeAQ4BCNEI0QLxAvELoPug8yDzIPoA6gDvoN+g09DT0NawxrDH0LfQt2CnYKZQllCUsISwgsBywHDwYPBvIE8gTZA9kDzALMAtEB0QHnAOcADAAMAD7/Pv92/nb+s/2z/f38/fxR/FH8rvuu+xT7FPt6+nr62/nb+T/5P/mo+Kj4FfgV+JD3kPcT9xP3mvaa9ir2KvbD9cP1YvVi9RL1EvXT9NP0nfSd9Hv0e/Rp9Gn0ZPRk9HP0c/SV9JX0wfTB9AD1APVN9U31o/Wj9Qj2CPZ19nX24vbi9lP3U/fF98X3Nvg2+Kz4rPgq+Sr5rfmt+Tr6OvrQ+tD6cPtw+xz8HPzU/NT8lf2V/Vz+XP4p/yn/+f/5/8kAyQCZAZkBZgJmAioDKgPjA+MDkgSSBDQFNAXLBcsFWgZaBtsG2wZPB08HuQe5BxQIFAhmCGYItAi0CPcI9wgwCTAJYQlhCYYJhgmkCaQJxgnGCeQJ5AkCCgIKIAogCjMKMwo9Cj0KQgpCCjoKOgomCiYKBgoGCtEJ0QmGCYYJLQktCcIIwghMCEwI0QfRB0wHTAfBBsEGNAY0BqoFqgUmBSYFsASwBEMEQwTfA98DgwODAywDLAPdAt0ClgKWAlQCVAIQAhACxQHFAWwBbAECAQIBiQCJAAMAAwBv/2//y/7L/hf+F/5R/VH9ePx4/Jb7lvuq+qr6tfm1+cP4w/jS99L35Pbk9gn2CfZB9UH1ifSJ9O3z7fNi82Lz4PLg8m7ybvIH8gfyofGh8UTxRPHj8OPwavBq8N7v3u817zXvZe5l7ofth+2a7JrsmeuZ65vqm+qa6ZrpkuiS6KTnpOfX5tfmLOYs5sDlwOWK5Yrld+V35ZXlleXf5d/lSuZK5vDm8ObL58vnyOjI6Ojp6OkZ6xnrSuxK7Ibthu3L7svuD/AP8FPxU/GH8ofymPOY84n0ifRj9WP1LfYt9vv2+/bU99T3tfi1+KL5ovmi+qL6wvvC+w39Df2S/pL+UABQADMCMwI1BDUETgZOBm0IbQicCpwK2wzbDBEPEQ84ETgRSBNIEycVJxXfFt8WgxiDGAAaABpgG2AbqhyqHLwdvB2jHqMedB90HyAgICC9IL0gXyFfIesh6yFpImki7CLsIlkjWSPDI8MjPiQ+JKkkqSQIJQglZCVkJZklmSWtJa0lsiWyJYoliiU6JTolzyTPJCokKiRSI1IjXSJdIjohOiH4H/gfrB6sHkcdRx3RG9EbXRpdGuAY4BhjF2MX9RX1FYoUihQhEyETvhG+EVMQUxDcDtwOXg1eDdEL0Qs0CjQKhwiHCMkGyQb0BPQEDgMOAx0BHQEi/yL/Iv0i/Sf7J/sw+TD5Pvc+92L1YvWb85vz6/Hr8WPwY/D97v3use2x7Y/sj+yP64/rquqq6vPp8+le6V7p2ejZ6HDocOgQ6BDoqOeo50nnSefo5ujmduZ25gnmCeaW5ZblFeUV5aDkoOQ25Dbk0+PT45LjkuNw43DjYuNi437jfuPC48LjIuQi5LTktORy5XLlSuZK5knnSedl6GXojumO6dHq0eon7CfsgO2A7ePu4+5H8EfwnfGd8ery6vIs9Cz0X/Vf9Yr2ivat9633x/jH+Nr52vnq+ur6+vv6+w39Df0u/i7+X/9f/50AnQDsAewBSQNJA6wErAQXBhcGigeKB/cI9whfCl8Kvwu/CwQNBA0xDjEOSw9LD0MQQxAeER4R5BHkEYQShBIDEwMTbxNvE7sTuxPzE/MTJRQlFEEUQRRPFE8UWRRZFFEUURQ/FD8UMBQwFBMUExTtE+0TwhPCE4ATgBMpEykTxhLGEksSSxK5EbkRGBEYEVoQWhB9D30PiQ6JDncNdw1NDE0MFQsVC9AJ0AmBCIEIMgcyB+QF5AWaBJoEWgNaAykCKQIHAQcB8//z/+3+7f7v/e/98fzx/PX79fv0+vT66Pno+df41/i597n3ifaJ9lT1VPUV9BX0zPLM8ovxi/FM8EzwC+8L79vt2+247Ljsneud66TqpOrH6cfpAOkA6WHoYejj5+PneOd45zfnN+cY5xjnDucO5yvnK+de517nkueS59Ln0ucP6A/oOug66GboZuiM6Izoouii6L7ovujg6ODoBukG6UrpSumz6bPpPuo+6v7q/urv6+/rBe0F7UnuSe6z77PvN/E38dTy1PKB9IH0LfYt9tD30Pdm+Wb55vrm+lD8UPyo/aj97P7s/hoAGgAxATEBMgIyAhsDGwPzA/MDxQTFBJEFkQVhBmEGPQc9ByAIIAgSCRIJHQodCjgLOAtuDG4MwQ3BDRwPHA98EHwQ3RHdESITIhNJFEkUUhVSFSMWIxa6FroWGxcbFzUXNRcQFxAXvxa/Fj4WPhadFZ0V7xTvFC0ULRRjE2MTpRKlEvER8RFWEVYR4xDjEIgQiBBDEEMQFBAUEOgP6A++D74Pmw+bD24Pbg8vDy8P1w7XDlAOUA6XDZcNsgyyDKMLowttCm0KGAkYCaAHoAcGBgYGVQRVBJwCnALqAOoATP9M/839zf1q/Gr8Ivsi+//5//kG+Qb5Ofg5+KD3oPcv9y/31vbW9pH2kfZW9lb2HvYe9vH18fXH9cf1kPWQ9U71TvX39Pf0hvSG9BD0EPSZ85nzJPMk87/yv/Jr8mvyJfIl8gDyAPL/8f/xH/If8mnyafLW8tbyWPNY8/Lz8vOh9KH0XPVc9Sb2Jvb49vj2yvfK9574nvhx+XH5Q/pD+hX7Ffvo++j7t/y3/IL9gv1K/kr+E/8T/97/3v+vAK8AiAGIAWQCZAJGA0YDLwQvBBoFGgULBgsGAgcCB/IH8gfYCNgIswmzCXMKcwoYCxgLpQulCw8MDwxYDFgMigyKDJ0MnQyaDJoMjgyODHQMdAxZDFkMSAxIDDwMPAw7DDsMSgxKDFoMWgxsDGwMhQyFDJgMmAyoDKgMtQy1DLAMsAyQDJAMVAxUDPEL8QtqC2oLyQrJCgoKCgovCS8JOQg5CCgHKAf+Bf4FywTLBJgDmANyAnICXgFeAWEAYQB8/3z/sP6w/gT+BP58/Xz9GP0Y/dr82vy3/Lf8pfyl/J78nvya/Jr8kfyR/IL8gvxk/GT8L/wv/OD74Ptx+3H74Prg+jT6NPpt+W35i/iL+Jn3mfeZ9pn2i/WL9YL0gvSA84Dzh/KH8qrxqvHq8OrwQvBC8MLvwu9j72PvGu8a7+/u7+7V7tXuv+6/7rjuuO607rTuoe6h7oPug+5G7kbu2+3b7VXtVe207LTs+uv660PrQ+uK6orqx+nH6RHpEels6Gzo3Ofc54fnh+dx53HnjeeN5+Xn5edn6GfoA+kD6crpyum+6r7q2OvY6x3tHe167nru0+/T7yXxJfFs8mzyo/Oj89f01/QD9gP2GPcY9xH4Efjq+Or4p/mn+V36Xfog+yD7+vv6+/D88PwC/gL+MP8w/3wAfAD3AfcBqgOqA4oFigWRB5EHsAmwCcgLyAvYDdgN5g/mD+MR4xHUE9QTuhW6FW8XbxfzGPMYUBpQGnYbdht6HHocbx1vHTseOx7lHuUeeB94H98f3x8zIDMgkyCTIO4g7iBTIVMhziHOITwiPCKmIqYiGyMbI34jfiPVI9UjJyQnJEokSiRBJEEkFyQXJLMjsyMjIyMjeCJ4IpshmyGTIJMgcR9xHyceJx7EHMQcXRtdG+gZ6BlnGGcY5xbnFloVWhXHE8cTPBI8ErIQshAtDy0Psg2yDTUMNQy0CrQKLwkvCaQHpAcNBg0GagRqBLsCuwL6APoAKf8p/1H9Uf1z+3P7kvmS+cD3wPf99f31S/RL9L7yvvJU8VTxB/AH8Ofu5+7r7evtBu0G7UjsSOym66brEesR65fql+op6inquOm46VHpUenp6Onoc+hz6AToBOiV55XnG+cb57HmseZQ5lDm7+Xv5ajlqOV15XXlUOVQ5VPlU+V+5X7lyuXK5U3mTeb95v3my+fL58HowejV6dXp+ur66jjsOOyG7Ybt0e7R7hrwGvBY8VjxgfKB8p/zn/Ov9K/0r/Wv9aL2ovaI94j3Yfhh+Df5N/kT+hP6+vr6+vH78fv6/Pr8E/4T/jz/PP97AHsA0wHTAT4DPgO8BLwERAZEBsUHxQc8CTwJqgqqCgMMAwxHDUcNdw53Dn4Pfg9fEF8QIhEiEbsRuxE3EjcSoxKjEvAS8BIlEyUTTRNNE1wTXBNfE18TZBNkE14TXhNSE1ITSRNJEzETMRMRExET8hLyEsQSxBKJEokSRRJFEuUR5RFsEWwR4BDgEDgQOBB0D3QPnQ6dDqgNqA2aDJoMfAt8C04KTgoWCRYJ3QfdB6MGowZqBWoFOAQ4BBADEAPxAfEB3ADcANL/0v/M/sz+x/3H/cX8xfy/+7/7sPqw+p35nfl/+H/4UPdQ9x32Hfbi9OL0nPOc81zyXPIg8SDx5O/k773uve6n7aftnuye7LTrtOvj6uPqJOok6orpiukP6Q/pqeip6GnoaehF6EXoL+gv6DfoN+hT6FPoc+hz6KXopeja6NroAOkA6SXpJelA6UDpR+lH6U7pTulR6VHpR+lH6UfpR+lY6VjpfOl86dHp0elk6mTqMOsw60XsReyb7ZvtI+8j79jw2PCx8rHymPSY9Ib2hvZv+G/4Q/pD+vr7+vuM/Yz99P70/jAAMABFAUUBOAI4Ag4DDgPPA88DgQSBBB0FHQWrBasFMQYxBrQGtAZEB0QH8gfyB7sIuwimCaYJtwq3CtwL3AsbDRsNeg56DuQP5A9TEVMRuxK7EvcT9xP5FPkUvxW/FTQWNBZbFlsWQxZDFt4V3hU6FToVbRRtFH0TfROEEoQSnxGfEcsQyxAREBEQdg92D+0O7Q59Dn0OLg4uDvgN+A3XDdcNyQ3JDbQNtA2PDY8NWg1aDQoNCg2hDKEMIQwhDH4LfguuCq4KsAmwCX4IfggdBx0HmwWbBQYEBgRoAmgC0ADQAEr/Sv/d/d39kvyS/HT7dPuF+oX6wfnB+Sr5Kvm0+LT4UPhQ+Pv3+/en96f3SPdI9+H24fZr9mv24vXi9VT1VPXF9MX0OfQ59MHzwfNf81/zDfMN89by1vK38rfyrfKt8sXyxfL98v3yUPNQ88HzwfNG9Eb01PTU9HD1cPUY9hj2x/bH9oD3gPc8+Dz47fjt+JD5kPkh+iH6oPqg+hb7FvuJ+4n7/Pv8+3T8dPz3/Pf8iP2I/S/+L/74/vj+5//n//UA9QAgAiACXQNdA54EngTeBd4FGQcZB0AIQAhNCU0JOQo5CvEK8Qp2C3YL0AvQCwAMAAwSDBIMEQwRDPcL9wvHC8cLiAuICzwLPAvxCvEKvgq+CqEKoQqhCqEKwQrBCvIK8goyCzILhQuFC98L3ws+DD4MngyeDOYM5gwMDQwNDQ0NDdsM2wx5DHkM7gvuCzULNQtPCk8KRglGCSAIIAjoBugGrgWuBXoEegRTA1MDPAI8AjgBOAFLAEsAfP98/9D+0P5H/kf+3P3c/Yn9if1H/Uf9EP0Q/ef85/zK/Mr8tPy0/KL8ovyH/If8WPxY/BX8Ffy6+7r7S/tL+9H60fpJ+kn6sfmx+Q/5D/li+GL4rfet9wL3Avdj9mP2zPXM9UH1QfW29Lb0IPQg9I/zj/MH8wfzi/KL8i3yLfLm8ebxpfGl8W/xb/E98T3xDPEM8e7w7vDf8N/w0PDQ8L7wvvCS8JLwOPA48MHvwe8u7y7vg+6D7tjt2O0q7Srtcexx7MTrxOsl6yXrmuqa6kLqQuof6h/qJeol6lrqWuqv6q/qEusS65Lrkusu7C7s4+zj7L3tve207rTuue+5783wzfDq8erxDPMM8zv0O/R19XX1qvaq9tH30ffc+Nz4wfnB+Yn6ifpB+0H7+fv5+7v8u/yR/ZH9ev56/nf/d/+UAJQA3gHeAVYDVgP+BP4EzgbOBqgIqAiCCoIKWAxYDBoOGg7TD9MPjBGMES4TLhO7FLsUNxY3Fo0XjRfJGMkY/Bn8GRAbEBsLHAsc9hz2HLMdsx1NHk0e1h7WHjofOh+KH4of3R/dHxsgGyBSIFIgkyCTIMMgwyDqIOogFiEWISohKiErISshJCEkIfkg+SCtIK0gTiBOIMYfxh8dHx0fZh5mHo4djh2bHJscmxubG30afRpHGUcZDBgMGL4WvhZfFV8V+BP4E3oSehLoEOgQTw9PD68Nrw0NDA0McQpxCtYI1gg3BzcHmAWYBfwD/ANjAmMC0ADQAEj/SP/C/cL9Pfw9/MD6wPpL+Uv53Pfc94T2hPY99T31BPQE9Oby5vLd8d3x4vDi8ADwAPAu7y7vX+5f7p/tn+3m7ObsKOwo7Hfrd+vL6svqGuoa6njpeOnf6N/oSuhK6M/nz+ds52znGecZ5+vm6+bd5t3m4+bj5g/nD+dZ51nns+ez5zDoMOjJ6Mnoc+lz6TrqOuoU6xTr9Ov06+Ts5Ozg7eDt3e7d7uLv4u/n8Ofw3vHe8czyzPKv86/ziPSI9GP1Y/VC9kL2IPcg9//3//ff+N/4xPnE+bf6t/rB+8H74vzi/BP+E/5S/1L/mwCbAOoB6gFFA0UDrwSvBBgGGAZ8B3wH1AjUCBEKEQo5CzkLVAxUDFkNWQ1IDkgOIg8iD9QP1A9jEGMQ3RDdEDoROhGGEYYRzBHMEfoR+hEWEhYSLRItEjMSMxIzEjMSNxI3Ei0SLRIXEhcS+RH5EcURxRGBEYERNxE3EdkQ2RBqEGoQ7w/vD1sPWw+zDrMOAA4ADjwNPA1pDGkMjwuPC6cKpwqwCbAJrgiuCJ8HnweBBoEGXAVcBTIEMgQEAwQD1QHVAagAqAB3/3f/Qf5B/gz9DP3X+9f7pPqk+n35fflb+Fv4Ofc59x32HfYB9QH14/Pj89Xy1fLU8dTx2/Db8Pnv+e8k7yTvVO5U7prtmu3v7O/sS+xL7L7rvus96z3rvOq86krqSurk6eTpgumC6TvpO+kI6Qjp3uje6MzozOjI6MjoxujG6Nbo1ujv6O/oA+kD6SHpIelA6UDpVOlU6XPpc+mf6Z/p1unW6TTqNOq+6r7qdet162zsbOyi7aLtC+8L76TwpPBf8l/yI/Qj9Oj16PWf95/3O/k7+bf6t/oN/A38Ov06/T7+Pv4i/yL/8f/x/7UAtQB8AXwBSwJLAh4DHgP2A/YDzwTPBKEFoQVzBnMGTgdOBy8ILwghCSEJKQopCjwLPAtfDF8Mlg2WDdEO0Q4TEBMQXRFdEZYSlhK0E7QTshSyFG4VbhXkFeQVGRYZFgAWABaiFaIVFBUUFVEUURRqE2oTdxJ3EnkReRGFEIUQrg+uD/EO8Q5VDlUO4g3iDYoNig1PDU8NMw0zDScNJw0nDScNMA0wDSwNLA0WDRYN7AzsDKAMoAwvDC8MlwuXC80KzQrNCc0JmQiZCDUHNQeqBaoFDAQMBGwCbALUANQAUP9Q/+j96P2f/J/8dvt2+3X6dfqb+Zv55fjl+FT4VPjc99z3cfdx9xP3E/e49rj2WvZa9gP2A/ay9bL1YvVi9SD1IPXp9On0t/S39JP0k/R49Hj0XPRc9Ej0SPQ69Dr0LfQt9C/0L/Q+9D70U/RT9Hb0dvSi9KL01PTU9Bj1GPV09XT16PXo9Xn2efYg9yD30ffR94n4ifhF+UX5A/oD+sf6x/qW+5b7avxq/ED9QP0Y/hj+7/7v/sf/x/+nAKcAjwGPAX0CfQJvA28DYARgBEYFRgUfBh8G6gbqBpwHnAczCDMIrwivCAkJCQlFCUUJbQltCX0JfQl/CX8Jegl6CWwJbAldCV0JWQlZCV8JXwl6CXoJtQm1CQgKCAp0CnQK+Ar4CoMLgwsPDA8MmgyaDA4NDg1lDWUNmw2bDZ8Nnw1tDW0NDA0MDXUMdQyvC68LyArICsUJxQmvCK8IlAeUB3oGegZoBWgFZQRlBHcDdwOfAp8C5AHkAUgBSAHJAMkAZQBlABcAFwDY/9j/pP+k/3X/df9J/0n/Hv8e//P+8/7D/sP+iP6I/kL+Qv7q/er9ff19/f/8//xw/HD8z/vP+yf7J/t3+nf6xPnE+Rn5Gfl4+Hj44vfi91/3X/fu9u72h/aH9jD2MPbh9eH1kvWS9Un1SfUC9QL1uPS49Hf0d/Q/9D/0DfQN9O7z7vPd893zz/PP88bzxvOy87LzgfOB8znzOfPP8s/yO/I78o7xjvHB8MHw0e/R79bu1u7V7dXt1+zX7P7r/utR61HrzerN6oLqgupj6mPqY+pj6o7qjurb6tvqOes5667rruss7Czsp+yn7DHtMe3R7dHtiO6I7mXvZe9f8F/wZfFl8XbydvKK84rzmvSa9Kn1qfWs9qz2j/eP90z4TPji+OL4Wvla+c35zflR+lH68vry+rf7t/un/Kf8x/3H/R7/Hv+0ALQAhgKGAnwEfASIBogGnQidCKgKqAqwDLAMvA68DrMQsxCTEpMSWhRaFO0V7RVcF1wXtRi1GOYZ5hnvGu8a0hvSG3AccBzZHNkcKR0pHVkdWR2BHYEdtR21Hd0d3R0IHggeTh5OHp4enh4JHwkflR+VHxggGCCNII0g+CD4ID4hPiFsIWwhkSGRIYshiyFXIVch/yD/IGsgayCsH6wf3B7cHusd6x3dHN0cuBu4G2caZxrwGPAYZxdnF8oVyhUiFCIUfBJ8Es0QzRAXDxcPZg1mDboLugseCh4KmwibCDIHMgfdBd0FmwSbBGUDZQMyAjICAAEAAdL/0v+h/qH+bP1s/TP8M/zu+u76mfmZ+UH4Qfjj9uP2gvWC9S30LfTc8tzyiPGI8T/wP/D+7v7uxO3E7arsquyq66rrt+q36uDp4Okc6RzpY+hj6NHn0edi52LnC+cL59rm2ubB5sHmruau5rbmtubU5tTm/+b/5krnSues56znFugW6Jfol+gp6SnpwOnA6Wrqauod6x3rzOvM64Psg+w/7T/t9e317bDusO5n72fvEfAR8LnwufBk8WTxE/IT8tTy1PKq86rzj/SP9In1ifWc9pz2yffJ9xT5FPl9+n36/fv9+4n9if0f/x//uwC7AFQCVALpA+kDcgVyBd0G3QYmCCYITQlNCUwKTAouCy4L/Qv9C7AMsAxKDUoN0Q3RDT0OPQ6aDpoO9w73Dk4PTg+kD6QPAhACEFYQVhCoEKgQ/xD/EFARUBGgEaAR9BH0ETsSOxJ1EnUSqBKoEsESwRLCEsISsRKxEoASgBIzEjMS0RHREVARUBGwELAQ9w/3DxwPHA8iDiIOFQ0VDfML8wu/Cr8KgQmBCTYINgjgBuAGiwWLBT0EPQT7AvsCyAHIAagAqACS/5L/hf6F/oT9hP2K/Ir8lvuW+6z6rPrD+cP50/jT+OL34vfp9un25vXm9eX05fTj8+Pz2vLa8tnx2fHY8Njw0u/S79ju2O7n7eft+ez57CLsIuxc61zroOqg6gLqAup+6X7pCekJ6bjouOiC6ILoWuha6FDoUOhZ6FnoYuhi6Hvoe+iY6Jjoq+ir6Mboxujk6OTo/+j/6C/pL+l46Xjp2enZ6Wvqa+oy6zLrKewp7FjtWO2z7rPuIfAh8JjxmPEF8wXzU/RT9IH1gfWN9o32dvd290f4R/gH+Qf5v/m/+Xv6e/pK+0r7NPw0/D/9P/1s/mz+sv+y/wQBBAFXAlcCpQOlA+QE5AQYBhgGSAdIB28IbwiVCZUJvQq9Ct4L3gv7DPsMGQ4ZDicPJw8lECUQExETEdkR2RF2EnYS7xLvEjQTNBNNE00TRxNHExYTFhPEEsQSYRJhEucR5xFoEWgR+RD5EJcQlxBLEEsQHBAcEPsP+w/pD+kP7g/uD/oP+g8PEA8QLRAtED4QPhA5EDkQHxAfEN4P3g92D3YP7A7sDjUONQ5PDU8NQQxBDAgLCAunCacJLAgsCJwGnAYCBQIFbANsA+kB6QGDAIMARP9E/zL+Mv5M/Uz9jPyM/PL78vt7+3v7Ivsi++b65vq9+r36mvqa+nb6dvpI+kj6B/oH+rz5vPlg+WD58Pjw+HL4cvji9+L3QPdA95r2mvb19fX1UfVR9bz0vPQ09DT0ufO581zzXPMg8yDzBfMF8xbzFvNN803znvOe8w70DvSY9Jj0OPU49fT19PXI9sj2q/er95n4mfiN+Y35e/p7+mD7YPs3/Df8+/z7/Kv9q/1G/kb+z/7P/kj/SP+z/7P/EwATAGkAaQC5ALkACgEKAV8BXwHBAcEBNQI1ArcCtwJIA0gD6APoA5AEkAREBUQFBwYHBtAG0AafB58HcwhzCDsJOwn4CfgJqgqqCkYLRgvQC9ALTQxNDLIMsgwADQANPA08DVkNWQ1YDVgNPQ09DfwM/AyaDJoMJAwkDJkLmQsGCwYLeQp5Cu0J7QllCWUJ5AjkCGMIYwjpB+kHfgd+Bx8HHwfMBswGggaCBjcGNwbnBecFlQWVBUIFQgXyBPIEpwSnBFkEWQQCBAIEoAOgAzIDMgO3ArcCNAI0AqgBqAEOAQ4BZwBnALb/tv8B/wH/UP5Q/qv9q/0S/RL9hPyE/AD8APyG+4b7FvsW+7n6ufpt+m36LPos+vP58/m1+bX5Zvlm+Qn5Cfmb+Jv4H/gf+KL3ovco9yj3rfat9jv2O/bJ9cn1UPVQ9df01/RX9Ff0y/PL8zvzO/Oc8pzy3/Hf8QzxDPEY8Bjw/O787tLt0u2e7J7sY+tj60PqQ+pD6UPpY+hj6MDnwOdb51vnK+cr50PnQ+eU55TnBegF6JjomOg66Trp1enV6XPqc+oM6wzrlOuU6xjsGOyU7JTs/+z/7G7tbu3n7eftau5q7gfvB++377fvZvBm8BXxFfG/8b/xZPJk8h3zHfP38/fz9PT09Bj2GPZf91/3wfjB+EP6Q/rv++/7x/3H/cH/wf/TAdMB6wPrA/EF8QXoB+gH2QnZCbsLuwuXDZcNbQ9tDyQRJBG+Er4SSBRIFK8VrxX/Fv8WOxg7GEUZRRkcGhwazhrOGk8bTxu8G7wbNRw1HK0crRw3HTcd4x3jHZgemB5lH2UfXCBcIFwhXCFnImcifiN+I3MkcyREJUQl+iX6JW8mbyaqJqomuCa4JngmeCbuJe4lMCUwJSgkKCTlIuUihCGEIfkf+R9WHlYethy2HA4bDhtuGW4Z6BfoF3IWchYOFQ4VwxPDE4ASgBJDEUMREBAQEN4O3g6tDa0NgwyDDFULVQscChwK1AjUCHkHeQcHBgcGhASEBPIC8gJOAU4Bmf+Z/9z93P0U/BT8R/pH+oX4hfjK9sr2FPUU9XHzcfPZ8dnxSvBK8Nfu1+577XvtK+wr7Prq+urg6eDp2uja6ADoAOhS51LnwebB5l/mX+Yc5hzm5OXk5cvly+XG5cblx+XH5dvl2+Xy5fLl9+X35fvl++X55fnl5+Xn5d/l3+Xd5d3l1eXV5eLl4uUF5gXmOeY55pjmmOYe5x7nvee954Loguhn6WfpY+pj6oXrhevI7MjsG+4b7oHvge/v8O/wWfJZ8sbzxvM49Tj1qvaq9h34HfiP+Y/5+vr6+l38Xfy6/br9Ef8R/10AXQCgAaAB2gLaAgUEBQQjBSMFOAY4BjwHPAc1CDUIKAkoCRAKEArzCvMK2gvaC7cMtwyRDZENbg5uDkAPQA8SEBIQ6hDqELgRuBGAEoASRhNGE/YT9hOWFJYULRUtFacVpxUDFgMWRxZHFlsWWxZDFkMWDRYNFq0VrRUqFSoVkhSSFNoT2hMHEwcTKRIpEjsROxFHEEcQWQ9ZD2kOaQ56DXoNkwyTDKsLqwvECsQK4QnhCfoI+ggPCA8IIAcgBysGKwYuBS4FLAQsBCUDJQMXAhcCAQEBAeX/5f++/r7+if2J/Uz8TPwC+wL7qPmo+Ur4Svjl9uX2d/V39Q70DvSp8qnyRPFE8fXv9e+87rzule2V7ZXsley167Xr6+rr6knqSerG6cbpVelV6QjpCOnS6NLon+if6H3ofehb6FvoJ+gn6PLn8uez57PnYOdg5xLnEufN5s3mk+aT5oXmhear5qvmBecF56bnpueD6IPogumC6Z3qneq567nrt+y37Jvtm+1e7l7uAO8A75fvl+8v8C/wyvDK8H3xffFT8lPyT/NP83v0e/TX9df1U/dT9+H44fhy+nL69vv2+2j9aP3K/sr+IAAgAGgBaAGnAqcC3QPdAwYFBgUnBicGRAdEB1MIUwhVCVUJSApICh8LHwvdC90LjAyMDCMNIw2rDasNLA4sDpsOmw4ADwAPaA9oD80PzQ89ED0QxBDEEFMRUxHsEewRkBKQEiYTJhOtE60TKBQoFIMUgxTCFMIU7hTuFPMU8xTUFNQUlxSXFC4ULhSgE6AT+hL6EjYSNhJZEVkRbRBtEGkPaQ9UDlQOPA08DSUMJQwaCxoLKQopCk8JTwmPCI8I8AfwB24HbgcKBwoHwgbCBoYGhgZIBkgG/gX+BZ0FnQUhBSEFiwSLBNkD2QMGAwYDDwIPAvkA+QDL/8v/k/6T/mT9ZP1B/EH8K/sr+yT6JPoo+Sj5N/g3+GP3Y/ez9rP2KvYq9tP10/Wn9af1m/Wb9bD1sPXc9dz1FPYU9lj2WPae9p722vba9g/3D/c49zj3T/dP9173Xvdj92P3X/df9133Xfdc91z3XPdc92L3Yvdw93D3hfeF96z3rPfp9+n3OPg4+Jz4nPgP+Q/5ivmK+RD6EPqj+qP6Q/tD+/D78Pup/Kn8a/1r/TX+Nf4W/xb/FwAXADwBPAGCAoIC1APUAw8FDwUVBhUG1QbVBkQHRAdxB3EHcwdzB1wHXAc9Bz0HHgceB/kG+QbNBs0GnwafBnMGcwZdBl0GcAZwBrIGsgYlByUHwgfCB3EIcQgmCSYJ1gnWCXAKcArqCuoKOgs6C0sLSwsaCxoLtQq1CikKKQqPCY8J+wj7CGkIaQjWB9YHPwc/B6EGoQYJBgkGigWKBSYFJgXbBNsEnQSdBGEEYQQnBCcE+QP5A9wD3APRA9EDzgPOA8MDwwOiA6IDbwNvAy4DLgPmAuYClwKXAjgCOAK6AboBGwEbAWIAYgCa/5r/z/7P/gn+Cf5I/Uj9i/yL/OH74ftY+1j7+Pr4+sf6x/qr+qv6ffp9+iD6IPpz+XP5afhp+BX3FfeD9YP1xvPG8wXyBfJJ8EnwlO6U7vns+exq62rr2OnY6VvoW+j65vrmwuXC5ePk4+Rm5GbkPeQ95GnkaeTD5MPkIOUg5XrleuW55bnlxuXG5bXlteWC5YLlLeUt5eHk4eSp5KnkheSF5IzkjOSv5K/k2eTZ5BvlG+V45Xjl9uX25bXmtea757vn/Oj86H3qfeo07DTsEe4R7hnwGfBH8kfyiPSI9ND20PYK+Qr5Jfsl+xn9Gf3w/vD+tgC2AHUCdQJABEAEHwYfBgkICQgCCgIKCwwLDBMOEw4fEB8QMBIwEiwULBQXFhcW+Bf4F7MZsxlXG1cb8RzxHGceZx7GH8YfHSEdIVMiUyJ6I3ojqySrJMYlxiXcJtwm+if6J/ko+SjlKeUp0SrRKpormitMLEws9iz2LGktaS2jLaMtsC2wLWQtZC3HLMcs8yvzK88qzypxKXEp+yf7J14mXiatJK0kBCMEI1IhUiGlH6UfEh4SHokciRwOGw4bpBmkGTIYMhiwFrAWKBUoFZETkRPyEfIRVxBXELQOtA77DPsMJAskCyUJJQn4BvgGqwSrBE0CTQLm/+b/gP2A/Sr7Kvvk+OT4rvau9pr0mvSg8qDyvfC98APvA+9o7Wjt4evh633qfeou6S7p4efh56zmrOaG5YblZ+Rn5G3jbeOO4o7iuOG44QLhAuFi4GLgz9/P32nfad8s3yzfA98D3wLfAt8U3xTfJt8m31PfU9+V35Xf2d/Z3zXgNeCX4Jfg6+Dr4EzhTOG+4b7hPuI+4uni6eK647rjoeSh5K3lreXb5tvmJOgk6Jnpmek36zfr7+zv7MHuwe6m8Kbwj/KP8n/0f/Rw9nD2WPhY+DH6Mfr7+/v7sv2y/VX/Vf/qAOoAcQJxAuMD4wNGBUYFnAacBuEH4QccCRwJVgpWCoMLgwuvDK8M4Q3hDQ8PDw9FEEUQihGKEckSyRIFFAUUQRVBFWQWZBZyF3IXcBhwGEQZRBnyGfIZhhqGGu4a7ho0GzQbahtqG3kbeRtjG2MbLxsvG8kayRo4GjgakhmSGc4Yzhj5F/kXJRclF0AWQBZQFVAVWxRbFFQTVBM/Ej8SJRElEQAQABDRDtEOoA2gDWcMZwwkCyQL3AncCY0IjQg5BzkH5QXlBZAEkAQyAzIDyAHIAVAAUADE/sT+Kf0p/Yv7i/vr+ev5SPhI+Kr2qvYK9Qr1Y/Nj88jxyPEz8DPwou6i7iftJ+29673rXOpc6h/pH+kF6AXoCOcI50DmQOai5aLlGuUa5bjkuORt5G3kJuQm5Pbj9uPM48zjleOV42TjZOMr4yvj2+Lb4o7ijuI84jzi3OHc4Y/hj+Fe4V7hT+FP4YzhjOEg4iDiAuMC4zvkO+S15bXlSedJ5+/o7+iL6ovqBuwG7GPtY+2d7p3usO+w763wrfCf8Z/xivKK8oPzg/OX9Jf0yvXK9Sb3Jvew+LD4YPpg+iz8LPwK/gr+7//v/8sBywGeA54DZQVlBRYHFgexCLEINwo3CpYLlgvQDNAM6w3rDdwO3A6uD64PbxBvEBcRFxGzEbMRVBJUEu4S7hKME4wTORQ5FN4U3hR/FX8VHRYdFqEWoRYNFw0XZxdnF58Xnxe/F78X1RfVF9EX0Re6F7oXlBeUF0cXRxfVFtUWSBZIFpoVmhXZFNkUFhQWFEYTRhNqEmoSiBGIEZcQlxChD6EPuQ65DtoN2g0DDQMNLAwsDEALQAs1CjUKEwkTCeQH5Ae3BrcGnAWcBZUElQSdA50DsAKwAswBzAHxAPEAIgAiAGH/Yf+q/qr+9f31/T/9P/2B/IH8ufu5++767voe+h76SPlI+XP4c/iY95j3tva29tj12PUA9QD1MvQy9IHzgfP28vbykvKS8mPyY/Jh8mHyf/J/8r3yvfIP8w/zafNp89Dz0PM+9D70qPSo9BP1E/V49Xj1z/XP9SP2I/Zx9nH2t/a39v72/vZE90T3ifeJ99T31Pcq+Cr4i/iL+AP5A/mV+ZX5QfpB+gb7Bvvg++D7xvzG/LD9sP2a/pr+gf+B/2IAYgA+AT4BFAIUAt8C3wKdA50DUwRTBP4E/gSqBaoFXQZdBhIHEgfFB8UHcAhwCAEJAQl4CXgJ2gnaCSQKJAphCmEKmQqZCsAKwAraCtoK6AroCuYK5greCt4K3QrdCuAK4ArtCu0KBAsECxcLFwsoCygLPAs8C0sLSwtbC1sLbAtsC3ALcAtfC18LOQs5C/cK9wqdCp0KNQo1CrwJvAkvCS8JkQiRCN0H3QccBxwHWwZbBqIFogX4BPgEYARgBNID0gNMA0wDzwLPAlwCXAL5AfkBqQGpAWcBZwErASsB7ADsAKAAoABAAEAAx//H/zb/Nv+E/oT+rv2u/bX8tfyY+5j7Xfpd+hv5G/ni9+L3wvbC9tD10PUO9Q71bvRu9O7z7vN683rzAPMA84byhvIE8gTycPFw8dDw0PAS8BLwIe8h7wruCu7I7MjsYeth6/np+eme6J7oU+dT50DmQOZn5WflyOTI5H7kfuSC5ILku+S75C7lLuW95b3lSOZI5tPm0+ZQ51Dnreet5/3n/ec76DvoXuhe6H/of+if6J/ouui66O3o7eg96T3pqumq6UjqSOoZ6xnrEewR7DntOe2L7ovu/u/+75zxnPFm82bzVvVW9WL3Yvd5+Xn5fft9+1n9Wf0H/wf/jACMAPUB9QFcA1wD0ATQBE8GTwbbB9sHdwl3CRYLFgvGDMYMlQ6VDm8QbxBTElMSNRQ1FOwV7BV3F3cX4RjhGB8aHxpKG0obexx7HJUdlR2gHqAeoB+gH3ggeCA3ITch9iH2IaIioiJMI0wjCCQIJLUktSRhJWElGiYaJr8mvyZVJ1Un4yfjJzcoNyhLKEsoIygjKJsnmyfAJsAmtCW0JXAkcCQUIxQjwSHBIWkgaSAWHxYf2h3aHaEcoRx5G3kbchpyGngZeBmGGIYYmheaF5cWlxZ6FXoVTxRPFBQTFBPLEcsReRB5EAUPBQ9hDWENjAuMC4oJiglsB2wHTgVOBUQDRANRAVEBeP94/7v9u/0W/Bb8ifqJ+iL5IvnZ99n3ovai9n31ffVV9FX0HfMd8+Px4/Gi8KLwW+9b7yTuJO757Pns0uvS68bqxurL6cvp3Ojc6BDoEOhg52DnxObE5k/mT+b25fblqeWp5XrleuVd5V3lQuVC5TvlO+U35TflIuUi5QrlCuXl5OXkquSq5H3kfeRk5GTkYuRi5JPkk+T15PXkeeV55S3mLeYI5wjn++f75xHpEelC6kLqfOt868TsxOwP7g/uUO9Q74vwi/DA8cDx6fLp8hD0EPQz9TP1T/ZP9mT3ZPd0+HT4evl6+X36ffqG+4b7mfyZ/Lv9u/3x/vH+NwA3AIQBhAHXAtcCKgQqBHMFcwWyBrIG5gfmBwUJBQkRChEKEQsRC/oL+gvTDNMMpQ2lDWQOZA4YDxgPyA/ID2YQZhD2EPYQfxF/Ee8R7xFLEksSmhKaEtAS0BLwEvASBRMFE/4S/hLdEt0SrBKsEl4SXhL5EfkRihGKEQcRBxF4EHgQ6A/oD1EPUQ+7DrsOMA4wDqQNpA0XDRcNiQyJDPAL8AtLC0sLnwqfCuYJ5gkdCR0JRwhHCFwHXAdcBlwGTAVMBTAEMAQKAwoD3QHdAawArAB0/3T/N/43/v78/vzK+8r7n/qf+or5ivmJ+In4mPeY97/2v/b49fj1O/U79ZD0kPTv8+/zS/NL863yrfIJ8gnyV/FX8abwpvDw7/DvMe8x73vue+7K7crtE+0T7WrsauzM68zrOes5683qzeqP6o/qf+p/6qzqrOoI6wjreet56/zr/Ot37Hfs1uzW7CLtIu1W7Vbtce1x7Yrtiu2l7aXtxu3G7QTuBO5i7mLu3u7e7obvhu9T8FPwNvE28S/yL/Iy8zLzMPQw9Cr1KvUe9h72CfcJ9/D38PfU+NT4sfmx+Yf6h/pS+1L7EPwQ/ML8wvxt/W39Fv4W/sP+w/57/3v/QgBCABUBFQH2AfYB5QLlAtgD2APPBM8ExwXHBa8GrwaCB4IHPAg8CNII0ghLCUsJtAm0CQ0KDQphCmEKtwq3CgYLBgtQC1ALnQudC+UL5QsyDDIMjQyNDOkM6QxHDUcNpw2nDfkN+Q1CDkIOjA6MDs0OzQ4KDwoPRA9ED2cPZw9uD24PWg9aDx8PHw/HDscOZw5nDgEOAQ6kDaQNXA1cDR0NHQ3kDOQMswyzDHoMegw8DDwMAAwADLwLvAtzC3MLJwsnC8wKzAplCmUK+An4CYIJggkECQQJgAiACO4H7gdKB0oHmQaZBt0F3QUfBR8FagRqBMIDwgMoAygDmwKbAhYCFgKUAZQBFQEVAZoAmgAhACEAq/+r/zX/Nf++/r7+Qv5C/sj9yP1R/VH94Pzg/Hr8evwc/Bz8v/u/+2T7ZPsH+wf7pvqm+kz6TPr9+f35ufm5+YX5hfle+V75PPk8+SX5JfkW+Rb5CvkK+Qf5B/kJ+Qn5CPkI+Qj5CPkI+Qj5BPkE+Qj5CPkU+RT5KPko+Uv5S/l++X75vvm++Qz6DPpl+mX6v/q/+hn7Gftw+3D7v/u/+wv8C/xV/FX8nvye/On86fw1/TX9gf2B/c79zv0c/hz+af5p/rb+tv4E/wT/WP9Y/7L/sv8aABoAkQCRABQBFAGhAaEBMgIyAr0CvQJBA0EDuQO5AyIEIgR6BHoExATEBPkE+QQeBR4FNwU3BUcFRwVWBVYFbgVuBY8FjwW8BbwF9QX1BTAGMAZuBm4GsQaxBvMG8wY4BzgHggeCB8QHxAf7B/sHJAgkCDYINggxCDEIGwgbCO8H7werB6sHTwdPB9YG1gZCBkIGnAWcBeoE6gQ5BDkEkgOSA/wC/AJ6AnoCCgIKAqgBqAFKAUoB5QDlAHEAcQDq/+r/Tv9O/6T+pP7r/ev9If0h/UX8RfxN+037NPo0+gf5B/nK98r3h/aH9lP1U/Ut9C30FPMU8xjyGPI68TrxevB68Orv6u+D74PvL+8v7+zu7O6g7qDuOO447sTtxO1H7Uftx+zH7F3sXewH7AfsuOu463rreutF60XrFesV6wLrAusO6w7rM+sz63jreOvO687rIewh7H7sfuzn7OfsY+1j7QnuCe7Y7tjuwO/A77XwtfCj8aPxdvJ28jjzOPP18/Xzu/S79J71nvWi9qL2ufe599343fgG+gb6Mvsy+2r8avy6/br9H/8f/4gAiADiAeIBGgMaAyUEJQQWBRYFCgYKBhMHEwc/CD8IhQmFCcIKwgrkC+QL5wznDMoNyg2lDqUOkQ+RD4IQghB0EXQRZBJkEjoTOhMDFAMU1hTWFKkVqRWDFoMWXhdeFxIYEhiWGJYY8RjxGBoZGhksGSwZRRlFGVwZXBl+GX4ZsBmwGdUZ1RnsGewZ/Bn8GfAZ8BnVGdUZuxm7GZEZkRldGV0ZJxknGdgY2Bh7GHsYIhgiGMQXxBdqF2oXGRcZF7IWshYsFiwWiRWJFb0UvRTdE90TAhMCEysSKxJhEWERqRCpEPIP8g88DzwPjg6ODt0N3Q0oDSgNbgxuDKALoAu6CroKxQnFCcMIwwjAB8AHyQbJBtoF2gXwBPAEBAQEBA4DDgMMAgwCAwEDAfr/+v/4/vj+//3//RX9Ff01/DX8Xvte+5b6lvrc+dz5Lvku+Y34jfjr9+v3Ofc593v2e/as9az10vTS9Ab0BvRO807zq/Kr8ijyKPK78bvxWvFa8Q7xDvHQ8NDwlvCW8GfwZ/A68DrwBfAF8Nfv1++w77Dvje+N737vfu9+737vge+B75Hvke+l76Xvs++z78Xvxe/X79fv4e/h7/fv9+8d8B3wUvBS8KTwpPAL8QvxdfF18ePx4/FO8k7yrfKt8g/zD/Nw83DzyPPI8xr0GvRh9GH0mvSa9Nf01/Qj9SP1f/V/9fH18fVu9m725vbm9lX3Vfe797v3GfgZ+H34ffjt+O34Zvlm+ej56Plq+mr65frl+lr7WvvI+8j7Lvwu/I38jfzj/OP8Lv0u/W79bv2m/ab93v3e/Rv+G/5l/mX+vv6+/iL/Iv+O/47/9//3/1cAVwCnAKcA5wDnABgBGAE9AT0BXQFdAXoBegGUAZQBrAGsAcABwAHNAc0B1gHWAdsB2wHgAeAB5gHmAe0B7QHzAfMB/AH8AQYCBgIWAhYCLAIsAkUCRQJaAloCZQJlAmACYAJPAk8CPAI8AjICMgI7AjsCXAJcAosCiwK9Ar0C5gLmAvsC+wL2AvYC3ALcArECsQJ7AnsCRQJFAhMCEwLsAewB1AHUAcwBzAHVAdUB8AHwARcCFwJGAkYCeAJ4AqMCowLBAsECzgLOAsgCyAK0ArQCmgKaAnwCfAJdAl0CPAI8AhcCFwLtAe0BwAHAAZMBkwFqAWoBSAFIASwBLAEXARcBBwEHAf0A/QD3APcA9gD2APYA9gDuAO4A2ADYAK8ArwBxAHEAJAAkANL/0v+D/4P/Pf89/wT/BP/U/tT+qv6q/oT+hP5g/mD+P/4//iD+IP4B/gH+3P3c/bL9sv2E/YT9WP1Y/Tj9OP0p/Sn9Kv0q/TP9M/05/Tn9L/0v/Rb9Fv3y/PL8z/zP/Lj8uPy0/LT8vvy+/NP80/zq/Or8//z//BT9FP0r/Sv9Qf1B/Vf9V/1k/WT9Y/1j/Vj9WP1K/Ur9Qv1C/Uv9S/1o/Wj9lP2U/cr9yv0A/gD+M/4z/mX+Zf6b/pv+2P7Y/h7/Hv9o/2j/sf+x//L/8v8qACoAWwBbAIkAiQCzALMA2ADYAPAA8ADzAPMA4wDjAMcAxwCvAK8ArQCtAMwAzAAMAQwBYgFiAbwBvAEPAg8CWAJYApoCmgLhAuECMwMzA4YDhgPTA9MDDwQPBDMEMwRJBEkEXQRdBHQEdASPBI8EqASoBLEEsQSnBKcEkgSSBHoEegRwBHAEfAR8BJcElwS7BLsE4wTjBAYFBgUoBSgFTgVOBXMFcwWUBZQFqwWrBbAFsAWnBacFmAWYBYcFhwV6BXoFdAV0BW4FbgVjBWMFVQVVBT8FPwUnBScFEAUQBfoE+gTlBOUE1gTWBMkEyQTBBMEEwATABMAEwAS+BL4EtgS2BKEEoQR+BH4EUgRSBB8EHwToA+gDtQO1A4QDhANXA1cDMQMxAxADEAP2AvYC4wLjAtQC1ALGAsYCuAK4AqUCpQKNAo0CcwJzAlcCVwI5AjkCGQIZAvMB8wHHAccBlQGVAV8BXwEsASwBAQEBAeEA4QDLAMsAvQC9ALAAsACcAJwAfwB/AFgAWAAoACgA8f/x/7D/sP9h/2H//f79/oX+hf77/fv9av1q/ef85/x8/Hz8L/wv/P/7//va+9r7rvuu+3T7dPsk+yT7vPq8+kb6RvrA+cD5J/kn+X74fvjB98H37vbu9hn2GfZG9Ub1e/R79MjzyPMp8ynzl/KX8hvyG/Kt8a3xQfFB8eHw4fCB8IHwFPAU8Knvqe877zvvx+7H7mPuY+4L7gvutu227XDtcO0x7THt8ezx7MXsxeyw7LDsseyx7Njs2Owc7RztZ+1n7brtuu0G7gbuQO5A7nvue+657rnu++777lHvUe+377fvIvAi8J7wnvAu8S7x0vHS8Z/yn/KV85XzrPSs9Nv12/UR9xH3Pfg9+Fv5W/lw+nD6f/t/+4z8jPyT/ZP9jP6M/m7/bv9CAEIAGQEZAQICAgIQAxADRAREBIQFhAXABsAG7QftBwIJAgkVChUKPgs+C3YMdgy6DboN9w73DggQCBDsEOwQthG2EW4SbhIyEzITFBQUFPcU9xTRFdEVnBacFj8XPxfRF9EXbxhvGBUZFRnNGc0ZmBqYGkkbSRvVG9UbPhw+HHAccByFHIUcmxybHKgcqBy3HLcczhzOHMwczByxHLEcjxyPHFccVxwgHCAc/Rv9G9Ub1RujG6MbZxtnGwUbBRuGGoYaBBoEGnYZdhnpGOkYYxhjGMoXyhcYFxgXWBZYFoAVgBWiFKIU0BPQE/0S/RIoEigSUBFQEWgQaBByD3IPfw5/DowNjA2aDJoMqAuoC6YKpgqMCYwJYAhgCCUHJQfqBeoFvAS8BKADoAOSApICjQGNAYsAiwCH/4f/g/6D/ob9hv2M/Iz8lPuU+6D6oPqm+ab5pfil+Kf3p/es9qz2tPW09cz0zPTt8+3zDPMM8zfyN/Jo8WjxnPCc8OPv4+867zrvmO6Y7gzuDO6M7YztEO0Q7ajsqOxN7E3s8evx66LroutV61Xr/ur+6rDqsOpp6mnqJ+on6gPqA+r56fnp/+n/6SLqIupT6lPqgeqB6rnquerz6vPqJ+sn62jraOux67Hr9ev160HsQeyK7IrsyezJ7BHtEe1m7WbtxO3E7TvuO+7D7sPuSu9K79fv1+9k8GTw7fDt8ILxgvEn8ify0vLS8ofzh/M79Dv04fTh9ID1gPUV9hX2ofah9iz3LPe197X3Ofg5+Lz4vPhA+UD5xPnE+Ur6SvrU+tT6Xvte++j76Ptz/HP8+vz6/H79fv39/f39dP50/uT+5P5S/1L/v/+//ywALACYAJgAAAEAAVwBXAGqAaoB7AHsASUCJQJZAlkCiwKLArkCuQLkAuQCCwMLAy8DLwNWA1YDigOKA80DzQMiBCIEjASMBP8E/wR0BXQF4wXjBToGOgZ1BnUGlwaXBqAGoAacBpwGmAaYBpQGlAaTBpMGmAaYBpoGmgaeBp4GpwanBrQGtAbHBscG4QbhBvoG+gYOBw4HHAccBx8HHwcaBxoHFQcVBwwHDAcBBwEH9gb2Bt8G3wa8BrwGkAaQBlkGWQYjBiMG+AX4BdcF1wW+Bb4FqAWoBYgFiAVaBVoFIwUjBeUE5QSkBKQEXwRfBA0EDQSlA6UDJAMkA5ECkQL6AfoBdQF1AQ0BDQHFAMUAlQCVAG0AbQBDAEMAEQARAN7/3v+v/6//if+J/2r/av9I/0j/F/8X/9b+1v6G/ob+L/4v/t393f2S/ZL9TP1M/Qb9Bv25/Ln8ZPxk/BD8EPzF+8X7i/uL+2f7Z/tV+1X7SvtK+0P7Q/s3+zf7Ivsi+wr7Cvvx+vH62PrY+sn6yfrD+sP6xfrF+tL60vrn+uf6APsA+x/7H/tE+0T7bPts+5n7mfvG+8b77vvu+xL8Evw1/DX8WfxZ/If8h/y//L/8//z//ED9QP17/Xv9q/2r/dP90/35/fn9If4h/lL+Uv6O/o7+0v7S/h3/Hf9t/23/v/+//xIAEgBlAGUAtwC3AAcBBwFYAVgBrAGsAQICAgJbAlsCtgK2AhADEANoA2gDwQPBAxgEGARrBGsEugS6BPsE+wQsBSwFUAVQBWcFZwV6BXoFlAWUBbMFswXYBdgFBAYEBiwGLAZRBlEGdQZ1BpYGlga2BrYG2AbYBvIG8gYFBwUHEQcRBxUHFQcXBxcHHAccBx8HHwcfBx8HGgcaBwYHBgfkBuQGuwa7BowGjAZdBl0GNAY0BgwGDAbjBeMFuQW5BYgFiAVRBVEFGQUZBd0E3QSfBJ8EYQRhBCIEIgTlA+UDrQOtA3kDeQNNA00DLAMsAxQDFAMGAwYDAAMAA/oC+gLvAu8C3ALcAr4CvgKWApYCawJrAjwCPAIMAgwC2QHZAZ4BngFaAVoBDgEOAb4AvgBwAHAAJwAnAOX/5f+n/6f/av9q/yz/LP/s/uz+qv6q/mn+af4m/ib+2v3a/YX9hf0i/SL9sfyx/D78PvzO+877ZPtk+wX7Bfum+qb6O/o7+sD5wPku+S75h/iH+Nn32fcm9yb3bPZs9rT1tPX59Pn0OvQ69I/zj/P/8v/yj/KP8kfyR/IV8hXy3PHc8ZTxlPEv8S/xrfCt8CrwKvC077TvUe9R7xDvEO/h7uHur+6v7n7ufu5F7kXuAu4C7srtyu2e7Z7td+137WPtY+1a7VrtVu1W7Wvta+2e7Z7t7O3s7WHuYe7t7u3ud+937/nv+e9l8GXwtvC28AHxAfFW8VbxvfG98UvyS/IA8wDzz/PP87b0tvSu9a71rPas9rL3sve++L74xvnG+cb6xvq5+7n7m/yb/HX9df1Y/lj+Vf9V/3MAcwCxAbEB/AL8AjYENgRQBVAFSAZIBiQHJAcCCAII/Aj8CA8KDwo6CzoLcQxxDJsNmw25DrkO2Q/ZD/MQ8xAMEgwSIxMjExQUFBTcFNwUhxWHFRAWEBabFpsWRhdGFwQYBBjWGNYYtRm1GXUadRoRGxEbkRuRG+Ub5RsiHCIcYRxhHJUclRzNHM0cGR0ZHV8dXx2iHaId7B3sHR4eHh46HjoeRx5HHigeKB7mHeYdkh2SHSIdIh2qHKocRRxFHOQb5BuKG4obPRs9G94a3hpqGmoa6hnqGUsZSxmUGJQY0RfRF/EW8Rb/Ff8VDRUNFRUUFRQjEyMTQhJCEl8RXxFvEG8Qbw9vD1EOUQ4aDRoN3AvcC58KnwpwCXAJWAhYCFIHUgdZBlkGZwVnBXcEdwSBA4EDgAKAAnABcAFIAEgABP8E/6z9rP1G/Eb84frh+pT5lPlj+GP4TfdN91L2UvZi9WL1bfRt9HrzevOF8oXyjPGM8aLwovDC78Lv4+7j7hXuFe5S7VLtkuyS7Ovr6+tR61Hrtuq26ibqJuqR6ZHp6ujq6EfoR+ir56vnFOcU56TmpOZY5ljmJ+Yn5iHmIeYx5jHmQOZA5ljmWOZr5mvmceZx5oLmguaf5p/mwebB5vvm++ZF50XnlOeU5/3n/ed86HzoCOkI6azprOlZ6lnq+Or46pLrkusm7CbstOy07FXtVe0Q7hDu2+7b7rzvvO+l8KXwh/GH8WbyZvI+8z7zC/QL9Nb01vSf9Z/1YfZh9iX3Jfft9+33uPi4+Iz5jPlo+mj6RvtG+yP8I/z5/Pn8w/3D/X3+ff4p/yn/zP/M/2gAaAAGAQYBqgGqAVACUAL4AvgCmwObAy4ELgSuBK4EGwUbBW8FbwWvBa8F4gXiBQsGCwYzBjMGZgZmBqcGpwb7BvsGZgdmB9kH2QdLCEsIswizCP0I/QgmCSYJMwkzCSgJKAkVCRUJCwkLCQgJCAkNCQ0JGAkYCRkJGQkPCQ8J/wj/COkI6QjSCNIIwQjBCKwIrAiSCJIIdgh2CFMIUwgxCDEIGggaCAkICQj6B/oH5wfnB74Hvgd5B3kHHQcdB60GrQY6BjoG1QXVBYMFgwVCBUIFCQUJBcUExQRuBG4EAwQDBIoDigMNAw0DkgKSAhgCGAKZAZkBFAEUAY0AjQARABEArv+u/2j/aP82/zb/Af8B/7n+uf5S/lL+0v3S/U/9T/3Z/Nn8ePx4/Cr8Kvzf+9/7jPuM+zX7Nfvh+uH6mvqa+mz6bPpP+k/6Nfo1+hf6F/rw+fD5wfnB+Zn5mfmB+YH5d/l3+Xz5fPmG+Yb5ivmK+Yr5ivmG+Yb5fvl++Xr5evl9+X35h/mH+Z/5n/nH+cf5/vn++Uf6R/qc+pz68Prw+j/7P/t++377qfup+8T7xPvZ+9n78Pvw+xX8FfxN/E38lfyV/Ov86/xJ/Un9qv2q/Q3+Df51/nX+4P7g/kz/TP+2/7b/HAAcAH4AfgDkAOQAUgFSAccBxwE/Aj8CsgKyAhMDEwNkA2QDrgOuA/gD+ANSBFIEwwTDBEEFQQXHBccFTAZMBsYGxgY3BzcHpwenBw8IDwhwCHAIxgjGCAQJBAktCS0JSQlJCV4JXgl4CXgJoQmhCdAJ0AkBCgEKMAowClAKUAplCmUKeAp4CoYKhgqRCpEKnQqdCpoKmgqKCooKcQpxCkoKSgobChsK6QnpCaoJqglbCVsJ/Aj8CIkIiQgJCAkIiweLBxMHEwemBqYGSAZIBvAF8AWZBZkFQgVCBeQE5ASABIAEGAQYBKkDqQM0AzQDuwK7AkICQgLNAc0BYAFgAQABAAGpAKkAWABYAAcABwCs/6z/Rf9F/9P+0/5Z/ln+3P3c/WT9ZP31/PX8jfyN/C/8L/zZ+9n7iPuI+0D7QPv++v76vfq9+n/6f/o++j769vn2+a/5r/lo+Wj5H/kf+dr42viS+JL4QPhA+Or36veQ95D3NPc09+P24/ab9pv2VvZW9hn2Gfbb9dv1kvWS9UX1RfXt9O30gvSC9A30DfSK84rz9/L38mvya/Lt8e3xgPGA8TXxNfEI8Qjx6/Dr8OPw4/Dj8OPw2fDZ8MnwyfCq8KrwcvBy8DbwNvD/7//v0u/S78Xvxe/T79Pv6O/o7wPwA/AU8BTwEPAQ8A3wDfAU8BTwJ/An8FfwV/Ce8J7w7vDu8FDxUPHA8cDxPvI+8tPy0/J783vzJfQl9Mv0y/Rh9WH14PXg9Vf2V/bY9tj2cvdy9zX4Nfgj+SP5Lvou+kf7R/te/F78a/1r/Wz+bP5k/2T/UwBTADABMAH5AfkBrgKuAlMDUwMDBAME1gTWBM8FzwXuBu4GHQgdCDQJNAkjCiMK7ArsCpULlQs9DD0MAQ0BDdsN2w3IDsgOww/DD60QrRCIEYgRWxJbEhsTGxPNE80TeBR4FAUVBRV5FXkV5RXlFUAWQBaiFqIWIRchF64XrhdKGEoY8RjxGHsZexneGd4ZHRodGiYaJhoPGg8a9xn3GdsZ2xnOGc4Z3BncGewZ7Bn8GfwZERoRGhEaERr/Gf8Z5BnkGagZqBlPGU8Z6xjrGHEYcRj0F/QXiReJFyIXIhfAFsAWZBZkFvQV9BVtFW0V1RTVFB8UHxRVE1UThRKFEqoRqhHQENAQChAKEFAPUA+mDqYOCQ4JDmMNYw2nDKcM0QvRC9kK2QrICcgJrwivCJgHmAeOBo4GmgWaBbsEuwTtA+0DLQMtA3MCcwK1AbUB7ADsABQAFAAm/yb/I/4j/hT9FP0B/AH89fr1+gH6Afon+Sf5Yvhi+K/3r/f89vz2NvY29mT1ZPWE9IT0nPOc88fyx/II8gjyW/Fb8cjwyPBF8EXwxe/F71XvVe/u7u7ug+6D7h7uHu6w7bDtK+0r7aLsouwZ7BnsmOuY6z7rPusN6w3r+er56gbrBusd6x3rJesl6ybrJusc6xzrBusG6//q/+oI6wjrHOsc60brRut863zrsOuw6/Xr9etH7EfsoOyg7AvtC+157Xnt1+3X7SruKu507nTuuu667hvvG++i76LvR/BH8AvxC/HY8djxlvKW8kbzRvPl8+XzdvR29Af1B/We9Z71N/Y39tn22faE94T3N/g3+Pb49vi/+b/5ifqJ+k77TvsD/AP8ofyh/Cv9K/2o/aj9IP4g/p3+nf4p/yn/wv/C/2UAZQAJAQkBpAGkAS0CLQKhAqEC/wL/AkgDSAOFA4UDvwO/A/0D/QNKBEoEsASwBCoFKgWyBbIFOwY7BqwGrAb4BvgGGgcaBxIHEgfxBvEGzQbNBrAGsAalBqUGsAawBsQGxAbcBtwG9Qb1BgMHAwcIBwgHBAcEB+4G7gbHBscGkwaTBlMGUwYPBg8G1AXUBaUFpQWDBYMFagVqBU0FTQUhBSEF4ATgBIkEiQQjBCMEuwO7A1kDWQMBAwEDtAK0AmkCaQIbAhsCxwHHAXEBcQEcARwBygDKAHgAeAAeAB4Atv+2/0L/Qv/M/sz+Z/5n/iX+Jf4G/gb+AP4A/v39/f3o/ej9tv22/XH9cf0q/Sr98Pzw/Mr8yvyu/K78jvyO/GP8Y/wy/DL8B/wH/PX79fv5+/n7BvwG/Ab8Bvzp++n7q/ur+2P7Y/sq+yr7EfsR+yP7I/tP+0/7gfuB+6r7qvvD+8P7zvvO+9b71vvi++L78Pvw+wD8APwT/BP8K/wr/FH8UfyK/Ir81fzV/Cv9K/1+/X79wv3C/fP98/0V/hX+L/4v/k7+Tv56/nr+tf61/v7+/v5Q/1D/p/+n/wAAAABaAFoAtwC3ABQBFAFxAXEBywHLAR8CHwJwAnACxQLFAiADIAOIA4gD/AP8A3EEcQThBOEERwVHBZwFnAXnBecFMAYwBngGeAbDBsMGEgcSB1wHXAekB6QH8AfwBzwIPAiJCIkI2AjYCBYJFgk/CT8JVwlXCVsJWwlaCVoJZQllCXkJeQmZCZkJvwm/CdoJ2gnqCeoJ9An0CfQJ9AnvCe8J6wnrCdsJ2wm9Cb0JlgmWCWQJZAkwCTAJBwkHCd8I3wi2CLYIggiCCDYINgjQB9AHWAdYB9YG1gZUBlQG3QXdBW4FbgUHBQcFpQSlBEQERATkA+QDhwOHAywDLAPQAtACcwJzAg8CDwKkAaQBNgE2AcgAyABgAGAAAAAAAKb/pv9N/03/7f7t/oT+hP4Q/hD+lf2V/R79Hv2v/K/8SvxK/PD78Pud+537S/tL+/76/vqx+rH6Y/pj+hf6F/rE+cT5ZPlk+fz4/PiM+Iz4F/gX+K33rfdR91H3/vb+9rn2ufZ29nb2LfYt9uT15PWY9Zj1RPVE9e/07/SR9JH0H/Qf9KLzovMV8xXzePJ48t/x3/FP8U/xxPDE8E/wT/Dr7+vvke+R71DvUO8l7yXvCO8I7wTvBO8L7wvvCe8J7wHvAe/i7uLupe6l7mHuYe4e7h7u5e3l7c7tzu3V7dXt6u3q7RDuEO457jnuXu5e7pbulu7l7uXuR+9H77/vv+878DvwpvCm8AnxCfFp8Wnx0/HT8V3yXfIH8wfzvvO+83X0dfQd9R31r/Wv9UH2Qfbq9ur2uPe497P4s/jQ+dD59/r3+hz8HPw3/Tf9S/5L/lz/XP9rAGsAcQFxAV0CXQItAy0D5gPmA5MEkwRTBVMFPAY8Bk8HTweJCIkI1wnXCRULFQs2DDYMOw07DR4OHg71DvUO1w/XD7kQuRCeEZ4RiBKIEl0TXRMoFCgU+hT6FMYVxhWUFpQWZRdlFxQYFBifGJ8YEBkQGWIZYhm0GbQZJxonGqoaqho9Gz0b1BvUG0McQxyFHIUcphymHJocmhx4HHgcWBxYHCocKhz5G/kb2BvYG7IbshuUG5QbjBuMG3sbextfG18bNRs1G+Ea4RpmGmYa2BnYGS8ZLxmBGIEY5BfkF00XTRfBFsEWQxZDFr0VvRUqFSoVjBSMFNET0RP7EvsSFxIXEh0RHREbEBsQJQ8lDz0OPQ5pDWkNrQytDPYL9gs2CzYLZApkCnMJcwloCGgIUAdQBzYGNgYlBSUFKAQoBD8DPwNnAmcCnQGdAd4A3gAgACAAXf9d/4/+j/6r/av9rPys/Jz7nPuD+oP6b/lv+Xn4efim96b38/bz9l72XvbR9dH1NvU29Y30jfTQ89DzAvMC8zryOvJ98X3xyPDI8CvwK/Cf75/vHu8e77juuO5m7mbuGe4Z7tft1+2L7YvtIu0i7azsrOwt7C3sreut607rTusW6xbr+ur66gLrAusX6xfrI+sj6y3rLest6y3rHuse6xXrFesT6xPrE+sT6ynrKetT61PrieuJ697r3utK7Ersv+y/7EHtQe2/7b/tJO4k7nrueu7A7sDu++777knvSe+177XvPPA88Obw5vCj8aPxXfJd8hLzEvO487jzSvRK9NT01PRY9Vj11vXW9Vj2WPbk9uT2efd59yH4Ifja+Nr4nvme+WT6ZPog+yD7xvvG+1P8U/zJ/Mn8MP0w/Zb9lv0H/gf+h/6H/hf/F/+x/7H/SwBLANwA3ABdAV0BzAHMASQCJAJuAm4CsQKxAvYC9gJJA0kDtgO2AzwEPATVBNUEeAV4BQsGCwaCBoIG0wbTBvkG+QYABwAH+Qb5Bu8G7wbwBvAGBQcFBykHKQdbB1sHlweXB9IH0gcJCAkIOQg5CFgIWAhkCGQIXgheCEIIQggZCBkI8QfxB9AH0Ae9B70HuAe4B7IHsgeeB54HcAdwBx8HHwewBrAGMwYzBrUFtQVABUAF3ATcBIIEggQtBC0E2wPbA4wDjANBA0ED/gL+ArgCuAJjAmMC8wHzAWEBYQG4ALgADAAMAHr/ev8R/xH/z/7P/qT+pP50/nT+Kf4p/sL9wv1I/Uj9zfzN/GD8YPz/+//7ofuh+0P7Q/vj+uP6ivqK+kr6Svoj+iP6CfoJ+uv56/mz+bP5Wvla+fH48fiP+I/4SPhI+Cv4K/g0+DT4T/hP+Hb4dvie+J74v/i/+N/43/j6+Pr4DPkM+R35Hfkx+TH5T/lP+YH5gfnJ+cn5Hfod+nn6efrU+tT6K/sr+4D7gPvT+9P7Ifwh/Gj8aPyq/Kr86/zr/Dj9OP2b/Zv9Fv4W/qT+pP49/z3/1f/V/2YAZgDwAPAAcwFzAesB6wFbAlsCxQLFAiwDLAObA5sDHAQcBKsEqwREBUQF3QXdBWQGZAbZBtkGQgdCB6AHoAf+B/4HYQhhCMEIwQgdCR0Jfgl+Cd0J3QlBCkEKrQqtChELEQtmC2YLqwurC9cL1wv0C/QLFAwUDDQMNAxdDF0MjwyPDLsMuwzdDN0M+Qz5DAYNBg0HDQcNAw0DDe4M7gzHDMcMkwyTDEwMTAz9C/0LsguyC2kLaQshCyEL2graCoQKhAoaChoKoQmhCRcJFwmDCIMI7AfsB1EHUQeyBrIGFAYUBnoFegXpBOkEZwRnBPID8gODA4MDFAMUA50CnQIeAh4CmwGbARoBGgGeAJ4AJQAlAK3/rf8s/yz/nf6d/gT+BP5i/WL9u/y7/Bj8GPx9+3376frp+mL6Yvrm+eb5cvly+Q/5D/m7+Lv4b/hv+C/4L/ju9+73oPeg90b3RvfZ9tn2WPZY9tL10vVJ9Un1v/S/9EL0QvTQ89DzaPNo8xfzF/PY8tjyn/Kf8m7ybvIx8jHy2PHY8WzxbPHp8OnwUfBR8Lnvue8j7yPvju6O7hDuEO6s7aztZO1k7UrtSu1V7VXtce1x7Zftl+2w7bDtp+2n7YztjO1m7WbtPO087S3tLe037TftUe1R7X/tf+2y7bLt2+3b7QfuB+4y7jLuWe5Z7onuie6/7r/u8u7y7jXvNe+O747vAPAA8JzwnPBd8V3xMPIw8gnzCfPU89TzgPSA9Bf1F/Wl9aX1NvY29t723val96X3iviK+I35jfmp+qn61fvV+w39Df1I/kj+ef95/5EAkQCIAYgBXQJdAhEDEQO6A7oDcgRyBEgFSAVMBkwGfgd+B8EIwQgBCgEKLAssCy0MLQwNDQ0N4g3iDa8Orw6ED4QPaxBrEE8RTxE0EjQSIBMgEwQUBBTmFOYUyxXLFZIWkhY3FzcXvhe+FxgYGBheGF4YshiyGBAZEBmIGYgZJBokGr8avxpTG1Mb2xvbGzMcMxxcHFwcYxxjHDocOhz4G/gbvhu+G4YbhhtlG2UbahtqG3cbdxuJG4kboBugG5sbmxt4G3gbPxs/G9sa2xpZGlka0RnRGUAZQBm4GLgYTRhNGOgX6BeDF4MXHBccF5QWlBbqFeoVJxUnFUAUQBRBE0ETPxI/EjwRPBFMEEwQfw9/D88Ozw4zDjMOoQ2hDfkM+QwtDC0MPws/Cy8KLwoKCQoJ4wfjB8QGxAa2BbYFwgTCBOsD6wMwAzADiwKLAu0B7QFBAUEBdQB1AIT/hP9x/nH+S/1L/S38Lfwn+yf7P/o/+nv5e/nO+M74KPgo+In3iffj9uP2L/Yv9nT1dPWs9Kz01/PX8wfzB/NF8kXylvGW8RPxE/G48LjwePB48FLwUvAs8Czw6+/r75Lvku8a7xrvhe6F7vXt9e147XjtEu0S7drs2uzE7MTsvOy87MXsxezO7M7sxuzG7LzsvOyp7KnshuyG7GjsaOxP7E/sPOw87EjsSOx17HXsu+y77CLtIu2W7ZbtAe4B7mTuZO607rTu6e7p7hjvGO9K70rvge+B79Lv0u8+8D7wu/C78E3xTfHm8ebxc/Jz8vPy8/Jf81/zsPOw8/Pz8/Mx9DH0cPRw9MT0xPQy9TL1t/W39Vb2VvYB9wH3p/en90L4QvjI+Mj4NPk0+Y/5j/nf+d/5K/or+n76fvre+t76SPtI+737vfs2/Db8p/yn/Aj9CP1X/Vf9lP2U/cv9y/0K/gr+Xv5e/sz+zP5R/1H/4//j/3EAcQDwAPAAVwFXAaMBowHXAdcB+QH5AQ4CDgIdAh0CLwIvAkcCRwJuAm4CrAKsAv8C/wJmA2YD2APYA0QERASgBKAE4wTjBAkFCQUZBRkFIgUiBSsFKwU/BT8FYAVgBYYFhgWpBakFwgXCBckFyQW/Bb8FrQWtBZIFkgV3BXcFYAVgBUsFSwU+BT4FPwU/BU8FTwVvBW8FmwWbBb4FvgXCBcIFmwWbBUQFRAXPBM8EXQRdBAkECQTgA+AD3gPeA+cD5wPjA+MDxQPFA5MDkwNcA1wDLwMvAwgDCAPaAtoClAKUAi4CLgK2AbYBRwFHAfUA9QDFAMUAqQCpAIcAhwBKAEoA8P/w/4b/hv8g/yD/zP7M/o7+jv5f/l/+NP40/g/+D/7x/fH93P3c/c/9z/3B/cH9pv2m/Xr9ev0+/T79+vz6/Lz8vPyL/Iv8avxq/Fr8WvxZ/Fn8X/xf/Gz8bPx3/Hf8e/x7/Hf8d/xy/HL8b/xv/Hn8efyU/JT8vfy9/Pb89vw+/T79kv2S/fL98v1X/lf+tv62/gP/A/86/zr/X/9f/3//f/+r/6v/6//r/z4APgCcAJwA/AD8AFgBWAGxAbEBDwIPAm4CbgLNAs0CJAMkA20DbQOsA6wD8QPxA0MEQwSwBLAENQU1BcEFwQVCBkIGrQatBvcG9wYtBy0HXgdeB5EHkQfMB8wHEAgQCEwITAiACIAIrgiuCNYI1ggBCQEJNAk0CWEJYQl+CX4JhQmFCW0JbQlECUQJHwkfCQQJBAn8CPwIBQkFCQoJCgkCCQIJ6AjoCLcItwh1CHUILgguCOAH4AeSB5IHSwdLBwoHCgfUBtQGqgaqBoUGhQZcBlwGLAYsBugF6AWOBY4FIwUjBacEpwQgBCAElgOWAxEDEQOVApUCJgImAsIBwgFkAWQBBAEEAZ8AnwAxADEAvP+8/0L/Qv/F/sX+Rv5G/sj9yP1M/Uz90fzR/Fj8WPzc+9z7UftR+7r6uvoQ+hD6VflV+Zj4mPjg9+D3Mvcy95v2m/YW9hb2nfWd9TP1M/XI9Mj0TvRO9Mfzx/Mm8ybzZfJl8pTxlPG18LXw0O/Q7wLvAu9W7lbu0e3R7YjtiO1u7W7tae1p7XHtce1l7WXtLe0t7djs2Oxs7Gzs9ev165frl+tZ61nrMesx6y3rLes+6z7rU+tT63nreeuk66Trw+vD693r3evr6+vr6evp6/Xr9esf7B/sbuxu7Pbs9uyr7avtc+5z7kXvRe8L8Avwt/C38FLxUvHi8eLxafJp8vry+vKh86HzZfRl9Fj1WPV99n32yPfI9yn5KfmH+of6yPvI+9/83/zM/cz9lv6W/kr/Sv/9//3/wADAAJ8BnwGpAqkC4gPiAzgFOAWcBpwG9gf2BycJJwktCi0KFgsWC+kL6QvBDMEMsA2wDawOrA66D7oP3BDcEPwR/BEbExsTNhQ2FCsVKxXyFfIVkRaRFvoW+hZNF00XrBesFxcYFxihGKEYUxlTGQ0aDRrIGsgaghuCGxIcEhxzHHMcqhyqHKMcoxx5HHkcURxRHDAcMBwvHC8cXxxfHJ8cnxzlHOUcLR0tHVEdUR1LHUsdIx0jHcAcwBwyHDIclRuVG+oa6hpQGlAa3xnfGYIZghkyGTIZ5hjmGHsYexjsF+wXQRdBF24WbhZ/FX8VhxSHFIYThhOREpESvxG/EQwRDBF4EHgQ+Q/5D20PbQ+/Dr8O6Q3pDeQM5Ay+C74LkAqQCmgJaAlVCFUIYgdiB40GjQbRBdEFJgUmBXsEewS/A78D5ALkAuMB4wG8ALwAfP98/zn+Of4H/Qf99vv2+xL7EvtX+lf6tfm1+SP5I/mO+I744/fj9yf3J/dS9lL2afVp9YD0gPSj86Pz2fLZ8jbyNvK38bfxTvFO8f/w//C18LXwXfBd8Pzv/O+I74jv++777m7ubu7p7entdO107SvtK+0N7Q3tCu0K7SHtIe067TrtPe097TLtMu0V7RXt5Ozk7LjsuOyV7JXsfOx87ILsguys7Kzs8ezx7F7tXu3f7d/tXe5d7tPu0+407zTvdu92767vru/k7+TvG/Ab8GbwZvDH8MfwNPE08bXxtfFA8kDyxvLG8kfzR/O587nzE/QT9F/0X/Sk9KT05/Tn9D31PfWq9ar1LfYt9sj2yPZu9273EvgS+Kz4rPg1+TX5qvmq+RL6Evp0+nT60vrS+jT7NPuZ+5n7/vv++2P8Y/zG/Mb8IP0g/W/9b/2t/a391/3X/fP98/0L/gv+K/4r/mL+Yv6z/rP+Gv8a/4n/if/v/+//PwA/AHIAcgCMAIwAlQCVAJYAlgCXAJcAmQCZAJ0AnQCkAKQAsACwAMYAxgDsAOwAJAEkAWcBZwGpAakB3QHdAfYB9gHzAfMB2gHaAbcBtwGaAZoBiwGLAYsBiwGVAZUBngGeAZwBnAGLAYsBbgFuAUkBSQEnAScBDwEPAQMBAwEDAQMBDgEOASMBIwFDAUMBbgFuAZsBmwG9Ab0BwQHBAZwBnAFQAVAB8ADwAJoAmgBmAGYAWwBbAG8AbwCMAIwAoACgAKYApgCpAKkAuAC4ANUA1QD3APcACQEJAf0A/QDWANYApQClAIEAgQB4AHgAhACEAJcAlwCeAJ4AkwCTAH8AfwBuAG4AaQBpAHIAcgCBAIEAlACUAKwArADMAMwA+AD4ACgBKAFQAVABZAFkAWIBYgFSAVIBQgFCATgBOAE2ATYBOgE6AUIBQgFSAVIBdAF0AagBqAHoAegBJQIlAlACUAJmAmYCcAJwAnkCeQKLAosCowKjArYCtgLAAsACxwLHAtkC2QIDAwMDSgNKA5gDmAPaA9oDAAQABAkECQQGBAYEDQQNBCgEKARTBFMEgwSDBKkEqQTEBMQE3QTdBPgE+AQXBRcFOAU4BUsFSwVOBU4FSwVLBUoFSgVaBVoFgQWBBbUFtQXtBe0FIgYiBkkGSQZlBmUGewZ7BocGhwaHBocGfQZ9BmcGZwZNBk0GOwY7BjAGMAYyBjIGPgY+BkkGSQZNBk0GSQZJBjcGNwYaBhoG9gX2BcsFywWcBZwFbwVvBUAFQAUTBRMF6AToBLwEvASNBI0EXwRfBC8ELwQBBAEE2QPZA7MDswOKA4oDWwNbAyADIAPcAtwClQKVAlICUgIWAhYC3wHfAaQBpAFcAVwBAgECAZsAmwAwADAAzP/M/3f/d/8v/y//8v7y/rv+u/6G/ob+Uf5R/h3+Hf7h/eH9lP2U/TP9M/23/Lf8Jfwl/Ij7iPvr+uv6UfpR+sf5x/lI+Uj50/jT+Gz4bPgO+A74sfex91j3WPf19vX2ffZ99vX19fVX9Vf1o/Sj9O3z7fM68zrzkfKR8gfyB/Kc8ZzxSPFI8RLxEvHn8OfwsvCy8HXwdfAj8CPwtO+070HvQe/Q7tDuZu5m7hjuGO7k7eTtvO287avtq+2j7aPtmO2Y7Zbtlu2T7ZPthu2G7X3tfe137Xftd+137ZTtlO3T7dPtLu4u7qzurO4+7z7vzu/O71zwXPDe8N7wSPFI8aXxpfH38ffxQ/JD8qHyofIg8yDzyPPI86X0pfSr9av1xfbF9uP34/f1+PX46/nr+cb6xvqJ+4n7Nvw2/Nj82Px//X/9Of45/hL/Ev8PAA8AKwErAVICUgJ1A3UDhQSFBHcFdwVRBlEGHgceB94H3geiCKIIdgl2CVkKWQpSC1ILZQxlDHsNew2HDocOfg9+D0cQRxDkEOQQaBFoEdAR0BE0EjQSrhKuEjQTNBPTE9MTkxSTFFkVWRUfFh8W3hbeFnIXchfXF9cXGBgYGC4YLhgxGDEYQxhDGF8YXxiVGJUY7hjuGEsZSxmnGacZ/hn+GTAaMBo8GjwaLhouGvUZ9RmhGaEZTRlNGfMY8xinGKcYexh7GFoYWhg+GD4YJRglGOoX6heKF4oXCxcLF2EWYRahFaEV4hTiFCcUJxSAE4AT+xL7EooSihImEiYSyRHJEVcRVxHHEMcQHhAeEFMPUw9zDnMOkg2SDbMMswzlC+ULMgsyC5cKlwoPCg8KkgmSCQkJCQlkCGQImwebB6wGrAagBaAFiQSJBHkDeQN6AnoCmAGYAdUA1QAuAC4Anv+e/xz/HP+X/pf+/f39/Un9Sf16/Hr8mfuZ+776vvr3+ff5SvlK+b34vfhE+ET40ffR92X3Zffz9vP2cvZy9uT15PVA9UD1hvSG9MrzyvMa8xrzg/KD8hryGvLX8dfxqfGp8YnxifFl8WXxLPEs8evw6/Cd8J3wP/A/8OXv5e+R75HvRe9F7xjvGO8M7wzvGe8Z70PvQ+9473jvou+i78Pvw+/P78/vwe/B76zvrO+Y75jvie+J75jvmO/F78XvBvAG8GDwYPDB8MHwF/EX8WTxZPGg8aDxxfHF8eTx5PH/8f/xGPIY8kPyQ/KE8oTy2fLZ8kjzSPPF88XzQPRA9LP0s/QW9Rb1YPVg9Z71nvXU9dT1CvYK9k/2T/an9qf2DvcO94X3hff/9//3a/hr+Mb4xvgG+Qb5Kfkp+Tr5OvlA+UD5SvlK+Wv5a/my+bL5Ifoh+rj6uPpo+2j7FvwW/K38rfwc/Rz9X/1f/YD9gP2N/Y39lv2W/ab9pv3G/cb99f31/TX+Nf6J/on+7/7v/mH/Yf/Z/9n/SQBJAKQApADiAOIABQEFARMBEwEaARoBKAEoAUQBRAFyAXIBrAGsAeYB5gEWAhYCMwIzAjkCOQIqAioCEgISAvoB+gHxAfEBAQIBAioCKgJoAmgCsQKxAvYC9gIrAysDRgNGA0ADQAMZAxkD1gLWAoUChQI5AjkCBgIGAvgB+AEQAhACPgI+AmwCbAKKAooCkwKTAo0CjQKGAoYChAKEAn0CfQJmAmYCOAI4AvkB+QHBAcEBqQGpAbsBuwHqAeoBHAIcAjQCNAImAiYC/AH8AcgByAGeAZ4BhQGFAXgBeAFxAXEBcgFyAX4BfgGaAZoBvwG/AdwB3AHgAeABxgHGAZcBlwFpAWkBUQFRAVgBWAF3AXcBpAGkAdIB0gH+Af4BJgImAkgCSAJdAl0CYQJhAlICUgI6AjoCKAIoAiMCIwIxAjECTwJPAnUCdQKkAqQC3wLfAiUDJQNvA28DsAOwA9cD1wPgA+AD2APYA80DzQPUA9QD9gP2AyoEKgRjBGMEmASYBMAEwATgBOAEAgUCBSMFIwVCBUIFYAVgBXgFeAWSBZIFugW6Be4F7gUvBi8GdQZ1BrAGsAbVBtUG5AbkBtoG2gbBBsEGqQapBpYGlgaMBowGjAaMBowGjAaJBokGhwaHBoQGhAaHBocGjwaPBo4GjgZ5BnkGTAZMBgYGBga3BbcFcQVxBTwFPAUYBRgF/QT9BNsE2wSrBKsEbgRuBCcEJwTbA9sDjQONAz4DPgPyAvICsQKxAoACgAJgAmACTAJMAjYCNgISAhIC2QHZAYkBiQEnAScBugC6AEkASQDZ/9n/cP9w/xL/Ev/C/sL+gf6B/k7+Tv4k/iT+/v3+/dr92v2y/bL9ff19/Tf9N/3U/NT8UvxS/L37vfsd+x37ffp9+un56fli+WL55Pjk+Hb4dvgV+BX4vPe89273bvcb9xv3tfa19jz2PPat9a31DfUN9XL0cvTg8+DzVPNU89ny2fJo8mjy/PH88afxp/Fn8WfxM/Ez8RHxEfHw8PDwvfC98H7wfvAt8C3wyO/I72jvaO8S7xLvx+7H7prumu6D7oPuc+5z7m/ub+5u7m7uY+5j7l7uXu5d7l3uXO5c7mzubO6O7o7uxO7E7iDvIO+i76LvPPA88Onw6fCT8ZPxIvIi8pjymPL18vXyPPM884TzhPPY89jzPfQ99MD0wPRm9Wb1KvYq9g33DfcD+AP49/j3+OD54Pmv+q/6X/tf+/T79Pt7/Hv8A/0D/aH9of1o/mj+XP9c/3cAdwCpAakB3ALcAvcD9wPyBPIEzQXNBYYGhgYqByoHxgfGB1wIXAj9CP0IuQm5CYsKiwpzC3MLZQxlDEENQQ35DfkNjg6ODvYO9g5ID0gPmw+bD+8P7w9XEFcQ5BDkEIwRjBFVElUSPBM8ExoUGhTaFNoUcBVwFcEVwRXcFdwV3BXcFckVyRW+Fb4V1hXWFQcWBxZXFlcWxxbHFjMXMxeJF4kXvxe/F7sXuxeGF4YXPRc9F+EW4RaIFogWRxZHFhEWERbtFe0V6BXoFewV7BXxFfEV7RXtFbkVuRVPFU8VwRTBFBMUExRnE2cT2xLbEmoSahIVEhUS3RHdEagRqBFxEXERMhEyEdIQ0hBIEEgQlQ+VD7kOuQ7IDcgN4gziDBMMEwxmC2YL4ArgCm4KbgoCCgIKkQmRCQUJBQlTCFMIfgd+B4oGigaKBYoFlwSXBL8DvwMMAwwDfAJ8AgMCAwKXAZcBLQEtAbsAuwA0ADQAjf+N/8X+xf7g/eD97/zv/A78DvxP+0/7t/q3+kj6SPrw+fD5nPmc+Ub5Rvni+OL4Z/hn+Nr32vc79zv3jvaO9u717vVq9Wr1CPUI9dP00/S/9L/0tPS09K30rfSd9J30e/R79FP0U/Qo9Cj0AvQC9Pfz9/MV9BX0W/Rb9Mn0yfRB9UH1l/WX9a31rfVr9Wv1zfTN9Prz+vMe8x7zZPJk8vrx+vHu8e7xMvIy8rLysvJI80jzzPPM8zP0M/R59Hn0pPSk9M70zvQC9QL1PvU+9YH1gfW79bv11vXW9dD10PWh9aH1SfVJ9d/03/R09HT0FfQV9Nnz2fPA88DzvvO+89Tz1PP38/fzHvQe9FP0U/SM9Iz0t/S39M30zfTC9ML0lPSU9GD0YPRA9ED0Q/RD9Hj0ePTU9NT0RPVE9cH1wfVD9kP2v/a/9jf3N/ej96P39/f39zr4Ovhq+Gr4h/iH+JL4kviE+IT4VvhW+Bj4GPjj9+P30PfQ9/n3+fdg+GD47/jv+JT5lPlC+kL6+Pr4+r37vfuX/Jf8fP18/V7+Xv4v/y//7f/t/5sAmwAzATMBqAGoAd4B3gHDAcMBYQFhAdkA2QBhAGEAIAAgACQAJABdAF0ArQCtAP0A/QBOAU4BqQGpARMCEwKLAosCCAMIA3sDewPnA+cDWgRaBNYE1gRYBVgF0QXRBSMGIwZFBkUGQAZABiEGIQb6BfoF0AXQBZgFmAVLBUsF7gTuBI0EjQQ/BD8EEgQSBP4D/gP1A/UD6APoA8wDzAOmA6YDggOCA2QDZANQA1ADSQNJA00DTQNiA2IDjwOPA8wDzAMLBAsEOgQ6BEkESQQzBDMEAwQDBMIDwgN6A3oDKwMrA88CzwJhAmEC5QHlAWUBZQHuAO4AiwCLAD0APQADAAMA2v/a/8H/wf+9/73/1v/W/w0ADQBfAF8AxwDHADgBOAGpAakBEAIQAmYCZgKiAqICvwK/ArYCtgKGAoYCNwI3AtMB0wFrAWsBDwEPAckAyQCdAJ0AiQCJAIcAhwCaAJoAxADEAAgBCAFsAWwB7gHuAYICggIgAyADvwO/A1QEVATdBN0EXAVcBckFyQUgBiAGYwZjBooGigaWBpYGjgaOBnEGcQZIBkgGHwYfBv8F/wX1BfUFCwYLBjoGOgZ5BnkGvQa9BvUG9QYiByIHSQdJB20HbQeWB5YHywfLBwQIBAhCCEIIhQiFCMIIwgjyCPIIEAkQCQ4JDgntCO0Iuwi7CIAIgAhICEgIHQgdCPAH8Ae6B7oHdAd0BxsHGwe3BrcGVgZWBv0F/QWwBbAFcQVxBToFOgULBQsF5gTmBMcExwSoBKgEhQSFBFgEWAQhBCEE5QPlA6ADoANIA0gDzgLOAiICIgI4ATgBEgASAMf+x/5q/Wr9EfwR/NX61fq7+bv5wPjA+On36fcp9yn3cfZx9r31vfX79Pv0HvQe9DHzMfMv8i/yG/Eb8QrwCvAA7wDvAO4A7iXtJe1w7HDs2evZ62jraOsE6wTrjOqM6v7p/ulJ6UnpYehh6Gnnaedq5mrmbeVt5Z7knuQF5AXknuOe43/jf+OV45XjveO94/Tj9OMh5CHkLuQu5DrkOuRQ5FDkfOR85OXk5eSP5Y/lbOZs5oLngue46Ljo6+nr6RTrFOsY7Bjs3uze7G/tb+3P7c/tDO4M7lPuU+7F7sXud+9374HwgfDf8d/xe/N78z/1P/UP9w/3zvjO+G/6b/rs++z7Sf1J/ZX+lf7r/+v/ZAFkAQ4DDgPvBO8E+gb6BgUJBQnvCu8KmQyZDOUN5Q3aDtoOkg+SDx4QHhCoEKgQWRFZETQSNBJOE04TrRStFC0WLRa/F78XUhlSGbMasxrXG9cbxhzGHGodah3lHeUdYR5hHuAe4B6EH4QfYiBiIFIhUiFGIkYiKyMrI8cjxyMRJBEkFyQXJMYjxiNAI0AjsSKxIh0iHSKpIakhdyF3IW4hbiGJIYkhvCG8IckhySGeIZ4hPCE8IY4gjiCxH7Ef0B7QHu4d7h0oHSgdlxyXHCUcJRzPG88bjhuOGzMbMxumGqYa2hnaGbIYshg2FzYXhBWEFa4TrhPYEdgRKhAqEKwOrA5pDWkNXwxfDHQLdAuOCo4KmAmYCX4Ifgg6BzoH2gXaBW4EbgQLAwsDxQHFAakAqQC3/7f/5f7l/iX+Jf5a/Vr9Zfxl/Dn7OfvH+cf5FPgU+ET2RPZ09HT0wfLB8lDxUPEm8CbwOe85747uju4K7gruj+2P7RvtG+2U7JTs5+vn6yvrK+tm6mbqoumi6QnpCemi6KLoZOhk6FnoWehp6Gnoc+hz6Hboduhb6FvoEugS6LXntedK50rn3Obc5pbmluaB5oHmm+ab5vTm9OZ553nnDegN6K7oruhI6UjpyOnI6T/qP+qx6rHqI+sj67Hrsetm7GbsO+077T3uPe5e717vifCJ8Lfxt/HW8tby0PPQ86P0o/RO9U711PXU9U32TfbO9s72Zvdm9yT4JPgI+Qj5BvoG+hH7Efsd/B38Hf0d/Q7+Dv7y/vL+zf/N/6UApQCCAYIBbgJuAmUDZQNpBGkEdAV0BXMGcwZcB1wHJggmCMMIwwg3CTcJiwmLCb4JvgndCd0J9wn3CQwKDAoiCiIKQgpCCmEKYQqCCoIKqAqoCswKzAr4CvgKOQs5C4sLiwv2C/YLeQx5DP4M/gx4DXgN2Q3ZDQcOBw79Df0Nvg2+DUMNQw2TDJMMugu6C7sKuwqmCaYJkwiTCJYHlgfEBsQGLQYtBs0FzQWYBZgFfAV8BWEFYQU8BTwFCQUJBcUExQRvBG8ECQQJBI0DjQP5AvkCTQJNAosBiwGuAK4Atv+2/6H+of5t/W39IPwg/M76zvqH+Yf5WfhZ+Fn3WfeG9ob22fXZ9Vf1V/X89Pz0vvS+9KH0ofSX9Jf0h/SH9Gn0afQr9Cv0xPPE80fzR/O/8r/yM/Iz8rvxu/FN8U3x3fDd8G3wbfD07/TvbO9s7+nu6e5y7nLuB+4H7r7tvu2W7Zbtie2J7aftp+3r7evtSe5J7sXuxe5R71Hv2e/Z72TwZPDs8Ozwb/Fv8fzx/PGQ8pDyH/Mf86vzq/Mo9Cj0jfSN9Ob05vQ99T31lvWW9f/1//V09nT27Pbs9mb3Zvfl9+X3b/hv+Bj5GPnv+e/59fr1+iT8JPxx/XH9yf7J/h8AHwBwAXABtwK3AugD6AMABQAF9wX3BcQGxAZwB3AHCQgJCJQIlAgZCRkJngmeCRYKFgqGCoYK+gr6CnQLdAsIDAgMvwy/DIsNiw1pDmkOVA9UDzkQORAhESERFRIVEgMTAxPnE+cTvBS8FGQVZBXeFd4VNxY3FmQWZBZyFnIWchZyFlIWUhYYFhgW0hXSFXcVdxUZFRkVzRTNFIcUhxRPFE8UKxQrFAUUBRThE+ETxhPGE6MToxN+E34TXxNfEzITMhP6EvoSuhK6El8SXxLpEekRWxFbEacQpxDRD9EP4w7jDtoN2g3BDMEMqAuoC5IKkgqICYgJkwiTCLAHsAfcBtwGFwYXBlkFWQWiBKIE9AP0A04DTgOzArMCIgIiApwBnAEcARwBnACcABkAGQCK/4r/6v7q/jj+OP5x/XH9lfyV/Kz7rPuy+rL6pfml+Yz4jPhm92b3NfY19g31DfXx8/Hz4fLh8u3x7fEO8Q7xP/A/8JHvke8A7wDvh+6H7jDuMO7k7eTtiO2I7RrtGu2D7IPstOu068Hqweqn6afpY+hj6BjnGOfR5dHlluSW5JrjmuPj4uPiaeJp4jniOeIz4jPiMOIw4jPiM+Is4iziDOIM4vTh9OHf4d/hw+HD4bfht+G04bThruGu4cThxOH24fbhNuI24primuIR4xHjg+OD4wPkA+SM5IzkHeUd5dbl1uW95r3mxefF5/bo9ug/6j/qheuF68rsyuwG7gbuLu8u703wTfBl8WXxdfJ18pDzkPPF9MX0IPYg9q33rfdu+W75VvtW+039Tf1A/0D/HAEcAdEC0QJjBGME2gXaBTgHOAeMCIwI5wnnCUMLQwuuDK4MMQ4xDrsPuw9OEU4R6hLqEm8UbxTfFd8VQhdCF4MYgxi5GbkZ+hr6GjUcNRx4HXgdyR7JHgMgAyAkISQhMSIxIgYjBiOqI6ojLiQuJHYkdiSbJJskvyS/JNck1yQAJQAlUyVTJa0lrSUPJg8meiZ6JsQmxCbvJu8mDCcMJ/cm9ya6JromZyZnJuUl5SVGJUYlqiSqJAAkACRUI1QjsiKyIvkh+SEkISQhQCBAIDYfNh8SHhIe7RztHLsbuxuLGosacRlxGV8YXxhZF1kXZBZkFmYVZhVVFFUULRMtE90R3RFmEGYQ2Q7ZDjkNOQ2UC5QL+wn7CXIIcgj+Bv4GnwWfBVEEUQQJAwkDvwG/AXEAcQAZ/xn/tv22/VL8Uvzt+u36iPmI+TL4Mvjp9un2qPWo9Xr0evRV81XzLvIu8hDxEPHy7/Lvzu7O7rrtuu217LXsuuu66+Hq4eol6iXqeul66fHo8eh56Hno/+f/55Hnkech5yHnouai5i3mLea85bzlSuVK5fDk8OSs5KzkduR25GbkZuR25HbkluSW5NXk1eQo5SjlfuV+5enl6eVh5mHm2uba5mjnaOcB6AHomeiZ6D3pPenm6ebphuqG6izrLOvV69Xreux67C3tLe3u7e7tuO647pXvle+E8ITwevF68YLygvKY85jzs/Sz9NX11fX39vf2EPgQ+CD5IPkr+iv6Mfsx+zX8Nfw3/Tf9Nf41/i3/Lf8gACAAEwETAQQCBAL5AvkC8APwA+EE4QTMBcwFsgayBowHjAdbCFsIIAkgCc0JzQlhCmEK3greCjwLPAuEC4QLxwvHCwMMAwxMDEwMtwy3DEENQQ3zDfMNyQ7JDp0PnQ9XEFcQ4xDjECcRJxEnEScR+hD6EKcQpxBAEEAQ1Q/VD1kPWQ/QDtAORA5EDrINsg0nDScNrwyvDEAMQAzXC9cLcwtzCwwLDAunCqcKUgpSCgkKCQrICcgJhgmGCSoJKgmmCKYI+Af4ByAHIAcjBiMGCgUKBdkD2QOOAo4CMgEyAdb/1v+L/ov+Y/1j/W78bvyn+6f7//r/+m36bfrg+eD5T/lP+cT4xPg8+Dz4tfe19zT3NPet9q32F/YX9nr1evXP9M/0FfQV9FvzW/Od8p3y0/HT8QzxDPFD8EPwde9177vuu+4e7h7upO2k7WPtY+1W7Vbtae1p7Zntme3R7dHt/O387SbuJu5H7kfuWe5Z7m3ube5/7n/uiu6K7qPuo+7K7sru+e757jjvOO9473jvqe+p79fv1+8D8APwMvAy8Hvwe/Dh8OHwWvFa8ezx7PGR8pHyQfNB8wf0B/Tg9OD0xfXF9bL2svag96D3h/iH+Gr5avlL+kv6KPso+wD8APzR/NH8mP2Y/Vn+Wf4Y/xj/1//X/5AAkABEAUQB8AHwAZgCmAJHA0cDDAQMBOYE5gTYBdgF3QbdBuYH5gf0CPQICwoLCiALIAs2DDYMSA1IDT0OPQ4SDxIPyQ/JD1kQWRDPEM8QOhE6EY8RjxHWEdYRGRIZEksSSxJ2EnYSpRKlEssSyxLzEvMSJxMnE1oTWhOVE5UT4hPiEy0ULRR0FHQUuRS5FOQU5BT3FPcU+hT6FNwU3BShFKEUVRRVFO0T7RNxE3ET8hLyEmgSaBLZEdkRThFOEbkQuRAcEBwQfw9/D9sO2w45DjkOpg2mDRoNGg2bDJsMKwwrDLwLvAtKC0oL1QrVClUKVQrLCcsJPAk8CaIIogj7B/sHRwdHB4MGgwawBbAF1wTXBP0D/QMhAyEDQgJCAlkBWQFZAFkAO/87/wD+AP6p/Kn8QftB+9/53/mT+JP4avdq93j2ePa29bb1FfUV9Yr0ivT68/rzTfNN84TyhPKT8ZPxbvBu8CjvKO+77bvtLOws7KTqpOo56Tnp++f75xHnEedx5nHm+eX55aHloeVB5UHlueS55BrkGuRi42LjkeKR4tDh0OEm4SbhkOCQ4DXgNeAS4BLgF+AX4FTgVOCs4Kzg/eD94EvhS+GG4YbhouGi4cPhw+H24fbhQOJA4sXixeKE44TjZ+Rn5HLlcuWI5ojmieeJ53joeOhN6U3pA+oD6rbqtupz63PrRuxG7E3tTe2Z7pnuK/Ar8AjyCPId9B30Q/ZD9lr4WvhE+kT67Pvs+1L9Uv2K/or+rf+t/9QA1AAaAhoCkAOQAzAFMAX2BvYG0wjTCKMKowpaDFoM7g3uDU4PTg+JEIkQtBG0EdES0RL7E/sTThVOFbwWvBZTGFMYEBoQGsIbwhtWHVYdux67HsQfxB95IHkg+SD5IEMhQyGAIYAh2SHZIUAiQCLEIsQibiNuIxMkEyStJK0kPSU9JZcllyW/Jb8lySXJJaEloSVjJWMlMiUyJf4k/iTbJNsk2iTaJNMk0yS+JL4klySXJDEkMSSKI4ojtiK2IqghqCF+IH4gXR9dH0AeQB46HTodWhxaHIYbhhu6Groa9Bn0GRUZFRkTGBMY8hbyFqQVpBUyFDIUtRK1EjIRMhG5D7kPXQ5dDhUNFQ3fC98LswqzCoAJgAk3CDcI1AbUBlIFUgW3A7cDEQIRAnAAcADe/t7+Y/1j/Qf8B/y/+r/6hPmE+Vb4Vvgp9yn38vXy9bj0uPR283bzLPIs8vDw8PDJ78nvvO687t3t3e0k7STte+x77OXr5etJ60nrkuqS6tDp0On+6P7oGuga6ETnROd+5n7mxOXE5TPlM+XG5MbkceRx5EzkTORL5EvkW+Rb5InkieTE5MTk+eT55DvlO+WG5Ybl0eXR5TXmNeaq5qrmH+cf56Hnoech6CHokeiR6ALpAulz6XPp4Ong6V/qX+rz6vPqluuW61rsWuw77TvtLO4s7jTvNO9I8EjwWvFa8W/yb/KB84HzjPSM9JX1lfWd9p32ofeh96X4pfir+av5svqy+rz7vPvH/Mf8zf3N/cX+xf6u/67/hACEAEkBSQEFAgUCuwK7AmkDaQMQBBAErwSvBEQFRAXeBd4FjgaOBloHWgdLCEsIYQlhCYIKggqfC58LqwyrDI4Njg1GDkYO2A7YDjkPOQ9vD28PhA+ED3EPcQ9DD0MPEA8QD9oO2g6zDrMOpw6nDqoOqg67DrsO1w7XDu8O7w4FDwUPJA8kD0IPQg9fD18PeQ95D3YPdg9MD0wPAg8CD5EOkQ4GDgYOcQ1xDcgMyAwLDAsMOQs5C08KTwpWCVYJYwhjCH8HfwexBrEG+wX7BVMFUwWxBLEEEQQRBHEDcQPQAtACMgIyApgBmAEBAQEBawBrANP/0/8w/zD/ev56/rH9sf3W/Nb87fvt+wX7Bfsd+h36Lfkt+Tn4Ofg69zr3M/Yz9j/1P/Vr9Gv0vvO+80LzQvPq8urypfKl8nfyd/JZ8lnyQ/JD8jzyPPIz8jPyEfIR8tvx2/GK8YrxIfEh8bvwu/BZ8Fnw+e/576Pvo+9P70/v9+737q7uru587nzuYO5g7m3ube6h7qHu8u7y7mfvZ+/27/bvj/CP8DHxMfHS8dLxZfJl8vPy8/J883zz9/P382r0avTQ9ND0JPUk9XL1cvXF9cX1IPYg9pH2kfYX9xf3q/er90v4S/j2+Pb4qPmo+WX6Zfos+yz7+fv5+8f8x/yX/Zf9Zv5m/jX/Nf8MAAwA7wDvANoB2gHMAswCvgO+A6AEoARqBWoFGAYYBqUGpQYdBx0HjQeNB/gH+AdjCGMI0QjRCDQJNAmPCY8J6gnqCUQKRAqpCqkKIgsiC6ILogsoDCgMtgy2DDsNOw27DbsNPg4+DrUOtQ4gDyAPgg+CD8oPyg/5D/kPGhAaECEQIRAWEBYQARABENkP2Q+iD6IPZg9mDx0PHQ/PDs8OjA6MDlIOUg4vDi8OLA4sDjwOPA5YDlgOdg52Dn0OfQ5mDmYONg42DuYN5g2CDYINFw0XDZ4MngwbDBsMkguSC/wK/ApYClgKpwmnCd8I3wj8B/wHAAcAB+8F7wXSBNIEuwO7A7cCtwLLAcsB9gD2ADEAMQBt/23/mv6a/rf9t/28/Lz8rfut+5f6l/p++X75Z/hn+GL3Yvdu9m72ivWK9cD0wPQF9AX0RvNG83/yf/KY8ZjxfPB88DnvOe/S7dLtVOxU7O3q7eqt6a3pneid6Nfn1+dV51XnAucC5+fm5+bq5urm7ubu5vXm9ebs5uzmwubC5pTmlOZm5mbmPuY+5jrmOuZV5lXmfOZ85rjmuObz5vPmGOcY5zbnNudE50TnPec95z/nP+dY51jnkeeR5xHoEejj6OPo/+n/6WnraesH7QfttO607lvwW/Dl8eXxPPM882X0ZfRr9Wv1W/Zb9k/3T/dc+Fz4ivmK+dv62/pG/Eb8t/23/Rv/G/9jAGMAiwGLAY4CjgJ/A38DcgRyBHUFdQWhBqEGCAgICJ4JngllC2ULUg1SDT8PPw8dER0R3BLcElcUVxSMFYwVhxaHFkEXQRfaF9oXeBh4GBcZFxnKGcoZmBqYGlsbWxsOHA4ctRy1HDEdMR2QHZAd5x3nHSQeJB5jHmMevx6/HisfKx+6H7ofdyB3IDghOCHxIfEhlyKXIvki+SIQIxAj6iLqInIiciK/Ib8h9yD3IBkgGSBCH0Ifjh6OHuod6h1XHVcd1xzXHEgcSBymG6Yb+hr6GjEaMRpYGVgZhBiEGK8XrxflFuUWNhY2Fo4VjhXnFOcUPRQ9FHETcRN6EnoSWRFZEQQQBBCFDoUO8QzxDFQLVAu+Cb4JRAhECOcG5wanBacFfgR+BGUDZQNPAk8COQE5ASEAIQAG/wb/5v3m/cv8y/y1+7X7qPqo+q35rfnA+MD42PfY9/b29vYK9gr2C/UL9QH0AfTp8unywvHC8aPwo/CM74zvfu5+7o/tj+277Lvs+ev561frV+vI6sjqP+o/6tDp0Ol06XTpJukm6fno+ejg6ODoy+jL6MToxOi86Lzop+in6Jfol+iE6IToZehl6E3oTeg26DboGOgY6AnoCegH6AfoDegN6C/oL+hr6GvotOi06BjpGOmP6Y/pCuoK6pnqmeo56znr4+vj66Pso+xx7XHtPu4+7g7vDu/Z79nvlfCV8E3xTfH/8f/xo/Kj8kTzRPPf89/zdfR19BP1E/W+9b71dfZ19jn3OfcE+AT4yvjK+Iv5i/lG+kb6/Pr8+rX7tft8/Hz8WP1Y/VT+VP57/3v/zQDNADoCOgKtA60DCAUIBSkGKQYABwAHjQeNB9MH0wfnB+cH4gfiB9IH0gfPB88H7gfuBzgIOAi5CLkIbwlvCUMKQwogCyAL9wv3C68MrwxLDUsN1w3XDU4OTg64DrgOFg8WD1cPVw98D3wPjw+PD4oPig90D3QPUw9TDxYPFg+9Dr0OTw5PDs8Nzw1SDVIN8gzyDLAMsAyNDI0MiAyIDIkMiQyFDIUMewx7DFkMWQwfDB8MzgvOC14LXgvWCtYKQQpBCqQJpAkHCQcJdAh0COYH5gdcB1wH0QbRBjwGPAaTBZMF0gTSBPMD8wP7AvsC9gH2AfcA9wAOAA4AR/9H/6f+p/4o/ij+vv2+/WP9Y/0I/Qj9ovyi/C/8L/yl+6X7A/sD+1L6UvqY+Zj53fjd+DD4MPiN94337Pbs9k/2T/aq9ar1+fT59Ev0S/Sm86bzD/MP85rymvJE8kTyBvIG8ufx5/He8d7x4PHg8fTx9PEP8g/yI/Ij8jXyNfI38jfyIPIg8v3x/fHP8c/xmfGZ8W7xbvFS8VLxRPFE8VDxUPF08XTxqvGq8fjx+PFY8ljyvPK88inzKfOZ85nzB/QH9H70fvQA9QD1h/WH9Rv2G/a39rf2U/dT9/T39PeZ+Jn4PPk8+d753vl5+nn6BvsG+4f7h/sA/AD8dfx1/O387fxs/Wz98v3y/Xv+e/4G/wb/j/+P/xYAFgCjAKMAOgE6Ad4B3gGTApMCWgNaAyoEKgT/BP8E1wXXBagGqAZyB3IHNAg0COEI4QhzCXMJ6wnrCT0KPQpyCnIKlwqXCq0KrQrCCsIK4wrjCgoLCgtCC0ILkguSC/ML8wtoDGgM8gzyDHsNew0ADgAOgQ6BDusO6w5AD0APgw+DD6gPqA+wD7APnw+fD2kPaQ8LDwsPiQ6JDtcN1w3+DP4MEgwSDCELIQtFCkUKlgmWCRQJFAm4CLgIdAh0CC0ILQjRB9EHVQdVB64GrgbcBdwF4wTjBM4DzgOpAqkCigGKAYUAhQCp/6n//v7+/oD+gP4Y/hj+qv2q/Rn9Gf1H/Ef8Jfsl+8D5wPks+Cz4hvaG9v70/vSo86jzj/KP8sDxwPEw8TDx0PDQ8Jvwm/B58HnwS/BL8AjwCPCd753vBu8G72LuYu7H7cftQ+1D7fLs8uzJ7Mnsr+yv7JzsnOx27HbsJ+wn7LzrvOs26zbrmOqY6gvqC+qh6aHpbOls6Y3pjekI6gjqzerN6tbr1usD7QPtKO4o7jHvMe8C8ALwi/CL8N/w3/AT8RPxQvFC8ZbxlvEm8iby9PL08gD0APQ29Tb1efZ59rj3uPfd+N342PnY+ar6qvpb+1v7+/v7+6P8o/xt/W39a/5r/qL/ov8PAQ8BowKjAkEEQQTSBdIFPwc/B28IbwhkCWQJLQotCtgK2AqJC4kLXAxcDE8NTw1lDmUOlA+UD7kQuRDJEckRvxK/EoMTgxMdFB0UnBScFPoU+hRWFVYV0hXSFWwWbBY4FzgXNxg3GEAZQBlAGkAaKRspG9Mb0xs9HD0cdhx2HHEccRxHHEccGxwbHOwb7BvSG9Ib5RvlGwccBxw2HDYcbBxsHIUchRx9HH0cXxxfHBYcFhywG7AbQRtBG74avho6GjoayRnJGV4ZXhn8GPwYpRilGDkYORixF7EXEBcQF0cWRxZkFWQVfhR+FJATkBOnEqcSzxHPEfoQ+hAqECoQYA9gD5AOkA62DbYN1QzVDOEL4QvbCtsKywnLCbIIsgiZB5kHjAaMBo0FjQWZBJkErQOtA8MCwwLUAdQB3gDeAOT/5P/m/ub+5/3n/ez87Pzx+/H79fr1+v35/fkH+Qf5EfgR+Cj3KPdJ9kn2cfVx9ar0qvTt8+3zM/Mz84byhvLh8eHxPfE98abwpvAS8BLwdu927+Hu4e5L7kvusu2y7SztLO217LXsRexF7O7r7uuj66PrWOtY6xzrHOvl6uXqpuqm6nDqcOo76jvqAeoB6tjp2OnB6cHptum26cfpx+nq6erpDeoN6jnqOepk6mTqgOqA6p/qn+q96r3q0urS6vTq9Oon6yfrZ+tn68nryetI7Ejs1+zX7Hvte+0k7iTuwO7A7lTvVO/Y79jvRPBE8KrwqvAL8QvxaPFo8c7xzvE98j3yq/Kr8iLzIvOo86jzPvQ+9Pn0+fTf9d/17vbu9iH4Ifhq+Wr5sfqx+uT75Pvz/PP8z/3P/Xb+dv7r/uv+OP84/2z/bP+e/57/4P/g/z8APwDHAMcAewF7AVYCVgJRA1EDYARgBG4FbgVvBm8GWwdbBycIJwjSCNIIZAlkCdYJ1gkvCi8Kewp7CrgKuAr3CvcKRAtEC5gLmAvxC/ELSgxKDIwMjAy4DLgM0gzSDNgM2AzaDNoM6AzoDAANAA0nDScNZQ1lDaUNpQ3iDeINFg4WDi0OLQ4lDiUOBQ4FDscNxw10DXQNGg0aDbgMuAxbDFsMEQwRDNYL1gumC6YLewt7CzsLOwvdCt0KYQphCscJxwkhCSEJgAiACOcH5wdbB1sH4QbhBnEGcQYMBgwGrwWvBU8FTwXgBOAEWwRbBLoDugMBAwEDOQI5AmwBbAGgAKAA1v/W/xP/E/9X/lf+p/2n/Qz9DP2B/IH8AfwB/If7h/sJ+wn7gvqC+vr5+vlw+XD55fjl+GD4YPjh9+H3aPdo9wD3APen9qf2U/ZT9gT2BPau9a71R/VH9dr02vRt9G30BPQE9LLzsvN683rzWPNY81XzVfNs82zzkfOR88fzx/MA9AD0LvQu9Fb0VvR19HX0h/SH9Jr0mvSv9K/0w/TD9OP04/QR9RH1SfVJ9ZH1kfXk9eT1NvY29oz2jPbl9uX2QvdC9673rvco+Cj4qviq+DP5M/nB+cH5T/pP+t/63/pu+277+Pv4+3n8efzz/PP8aP1o/d/93/1g/mD+6v7q/nr/ev8PAA8ApwCnAEIBQgHlAeUBkQKRAkADQAPtA+0DlASUBCoFKgW2BbYFPQY9Br4GvgZCB0IHzgfOB1sIWwjsCOwIhQmFCRgKGAqjCqMKJQslC5ELkQvqC+oLNQw1DGkMaQyNDI0MqgyqDLgMuAy6DLoMuAy4DKEMoQx1DHUMOAw4DOcL5wuQC5ALRQtFCwYLBgvSCtIKngqeClUKVQrqCeoJXgleCbIIsgjrB+sHEgcSByUGJQYpBSkFKQQpBDgDOANqAmoCzQHNAV4BXgEHAQcBqQCpACUAJQBk/2T/X/5f/if9J/3O+877aPpo+hb5Fvnj9+P32fbZ9gb2BvZn9Wf17fTt9Jb0lvRH9Ef05fPl82rzavPM8szyC/IL8kXxRfGM8Izw7O/s73zvfO817zXvBO8E7+bu5u7H7sfuku6S7k/uT+737ffthu2G7RbtFu247LjseOx47HrseuzC7MLsQe1B7fLt8u237rfube9t7w3wDfCI8Ijw1fDV8AbxBvEn8SfxSPFI8Yvxi/ED8gPytfK18qXzpfO/9L/04vXi9fj2+Pbu9+73t/i3+Fv5W/nm+eb5Zfpl+u367fqR+5H7Xvxe/Fv9W/2J/on+2P/Y/zABMAF8AnwCqQOpA6YEpgR6BXoFMAYwBtUG1QaAB4AHRwhHCCoJKgkuCi4KTgtOC28MbwyEDYQNhg6GDl4PXg8SEBIQshCyEDkRORG/Eb8RWhJaEgQTBBPIE8gTrRStFJUVlRV4FngWUBdQF/oX+hd3GHcY0RjRGP0Y/RgRGREZKxkrGUcZRxl2GXYZyBnIGSMaIxqBGoEa4hriGiIbIhtBG0EbShtKGykbKRvsGuwaqRqpGlQaVBoAGgAawBnAGYMZgxlMGUwZIBkgGd0Y3RiAGIAYDBgMGHEXcRe7FrsWABYAFj0VPRV+FH4U0RPREyYTJhN9En0S2BHYESgRKBFsEGwQpw+nD80OzQ7hDeEN7AzsDOsL6wvpCukK8QnxCQQJBAkfCB8IQAdAB1wGXAZqBWoFbARsBGEDYQNNAk0CNwE3AScAJwAd/x3/G/4b/if9J/08/Dz8V/tX+376fvqo+aj50fjR+AD4APgw9zD3XvZe9pP1k/XJ9Mn0+/P78zLzMvNp8mnymPGY8c7wzvAF8AXwOO8473zufO7R7dHtMu0y7bTstOxQ7FDs++v767/rv+uP64/rXetd6zLrMusE6wTrxOrE6obqhupG6kbqAeoB6tDp0Omt6a3pkOmQ6YbphumJ6YnpjOmM6aPpo+nJ6cnp8+nz6S7qLupx6nHqs+qz6gbrButn62fr0OvQ603sTezU7NTsVO1U7dXt1e1P7k/uu+677invKe+Y75jvAfAB8HPwc/Ds8OzwaPFo8fLx8vGH8ofyHPMc87TztPNG9Eb0x/TH9D71PvWt9a31GPYY9pP2k/Yv9y/3+Pf49/z4/Pg8+jz6qvuq+y39Lf2h/qH+4f/h/80AzQBXAVcBhAGEAWgBaAEmASYB6ADoAM8AzwD7APsAewF7AUwCTAJqA2oDwATABCsGKwaQB5AH2AjYCOUJ5Qm2CrYKVgtWC8cLxwsdDB0MagxqDKoMqgzlDOUMHw0fDU0NTQ1zDXMNlA2UDaQNpA2kDaQNng2eDY4Njg2BDYENiA2IDaANoA3SDdINIw4jDoMOgw7qDuoOTQ9ND4wPjA+VD5UPYw9jD+wO7A5BDkEOgw2DDcEMwQwRDBEMgAuACwQLBAuWCpYKMQoxCsgJyAlUCVQJ0gjSCDgIOAiIB4gHzgbOBhUGFQZxBXEF8gTyBJkEmQRcBFwELAQsBO8D7wOQA5ADBAMEA0kCSQJiAWIBXQBdAE3/Tf9A/kD+RP1E/WT8ZPyi+6L7/fr9+nr6evoR+hH6t/m3+Wj5aPkV+RX5sviy+Eb4RvjR99H3V/dX9+r26vaI9oj2KvYq9tL10vV29Xb1DvUO9aP0o/Q09DT0vfO980zzTPPh8uHyevJ68ivyK/L48fjx5PHk8f7x/vFB8kHyn/Kf8hbzFvOS85Lz//P/81r0WvSc9Jz0xPTE9OP04/QA9QD1IPUg9VD1UPWO9Y711vXW9S32LfaR9pH2/fb99nb3dvf59/n3gPiA+A35Dfmj+aP5QvpC+vH68fqz+7P7gvyC/Fj9WP0q/ir+7P7s/pf/l/8qACoAqACoABcBFwF+AX4B5QHlAU4CTgLBAsECQwNDA9YD1gOABIAERAVEBRUGFQbuBu4GxgfGB4wIjAg8CTwJ2wnbCWIKYgrZCtkKSgtKC6wLrAsHDAcMYgxiDLYMtgwIDQgNYg1iDbUNtQ0FDgUOVg5WDpkOmQ7QDtAOAg8CDyMPIw83DzcPRA9EDzwPPA8fDx8P8g7yDqcOpw5GDkYO2w3bDWUNZQ3tDO0MfQx9DAcMBwyHC4cL+wr7ClkKWQqjCaMJ4AjgCAwIDAgnBycHMQYxBi0FLQUlBCUELQMtA1QCVAKiAaIBEAEQAYoAigDv/+//J/8n/yr+Kv73/Pf8mfuZ+yn6Kfqy+LL4Pvc+9+X15fWv9K/0ovOi88/yz/Is8izyofGh8SbxJvGd8J3w9u/27z7vPu997n3uu+277RftF+2T7JPsJuwm7Nnr2euc65zrWOtY6xPrE+u76rvqROpE6sHpwek16TXpp+in6D7oPugH6AfoBegF6FHoUejg6ODomuma6Xnqeepf61/rLewt7ODs4Oxu7W7t0O3Q7SLuIu5x7nHuy+7L7kzvTO/77/vvzvDO8MXxxfHN8s3yzvPO88P0w/Sm9ab1c/Zz9jf3N/cB+AH41/jX+Mj5yPne+t76Gvwa/Hb9dv3q/ur+YABgAMEBwQH8AvwCCwQLBOwE7ASyBbIFdAZ0Bj8HPwclCCUILwkvCVAKUAqCC4ILvwy/DO0N7Q0EDwQPBRAFEN0Q3RCaEZoRTxJPEvoS+hKwE7AThxSHFG0VbRVoFmgWdxd3F3cYdxhgGWAZMBowGsgayBowGzAbeht6G5wbnBuxG7Eb1BvUG/ob+hsxHDEchByEHNMc0xweHR4dah1qHZgdmB2uHa4dux27Haodqh2GHYYdYx1jHSwdLB3vHO8cvRy9HH4cfhw3HDcc6xvrG34bfhvvGu8aTBpMGokZiRm4GLgY8BfwFyYXJhdhFmEWqBWoFeoU6hQoFCgUbRNtE6oSqhLiEeIRFxEXEToQOhBLD0sPUQ5RDkkNSQ04DDgMJwsnCxAKEAryCPII0AfQB6cGpwZ7BXsFVARUBDQDNAMbAhsCBwEHAfn/+f/p/un+1v3W/cT8xPyy+7L7nvqe+pT5lPmQ+JD4kfeR96L2ova89bz12PTY9P/z//Mn8yfzR/JH8mvxa/GM8IzwpO+k78buxu7w7fDtHu0e7WTsZOy767vrGusa65DqkOoU6hTqmOmY6SzpLOnF6MXoWOhY6Prn+ueo56jnW+db5y7nLucZ5xnnD+cP5xznHOcx5zHnOec550PnQ+dF50XnNuc25y7nLucs5yznK+cr50TnROd253bnu+e75yXoJeit6K3oQelB6efp5+mP6o/qJusm67brtus+7D7svuy+7EztTO3t7e3tme6Z7lnvWe8k8CTw7fDt8LjxuPGA8oDyPfM98/Xz9fOm9Kb0SvVK9e/17/Wa9pr2S/dL9wr4CvjS+NL4lvmW+VL6UvoF+wX7r/uv+1r8WvwW/Rb97v3u/e3+7f4aABoAcQFxAeAC4AJPBE8EnQWdBaUGpQZTB1MHpAekB6AHoAdkB2QHFwcXB9YG1gbHBscGBAcEB5UHlQeCCIIIxgnGCTkLOQu7DLsMJw4nDkoPSg8QEBAQfBB8EI4QjhBgEGAQFRAVELQPtA9RD1EP/A78DrIOsg5+Dn4Oaw5rDm4Obg5/Dn8Olg6WDpcOlw5/Dn8OVg5WDiAOIA7vDe8N0w3TDb4Nvg2kDaQNeQ15DSQNJA2jDKMMAAwADDwLPAthCmEKewl7CYwIjAiiB6IH0AbQBiIGIgaeBZ4FQQVBBfYE9gSkBKQENgQ2BJ4DngPZAtkC8gHyAfsA+wAGAAYAJf8l/2n+af7V/dX9ZP1k/Q79Dv2+/L78X/xf/Ov76/tY+1j7qPqo+uj56Pkd+R35TvhO+Iv3i/fZ9tn2PfY99sf1x/V39Xf1Q/VD9Sr1KvUc9Rz1CvUK9fX09fTY9Nj0sPSw9Ij0iPRd9F30KfQp9Pbz9vPE88TzkvOS83DzcPNe817zWPNY82LzYvN083TzgvOC85nzmfO9873z9PP08070TvTM9Mz0Y/Vj9Q72Dva99r32YPdg9/T39Pdy+HL41fjV+CT5JPlh+WH5lPmU+cz5zPkX+hf6ffp9+gb7Bvu0+7T7ffx9/Fr9Wv1A/kD+JP8k//3//f/IAMgAhgGGATQCNALZAtkCewN7AxcEFwSxBLEETAVMBeMF4wV4BngGDAcMB5UHlQcTCBMIiQiJCO8I7whRCVEJvQm9CTEKMQq6CroKXwtfCxEMEQzODM4Mkw2TDUYORg7iDuIOZQ9lD70PvQ/xD/EPEBAQEBQQFBAMEAwQCBAIEAEQARD/D/8PDBAMEBwQHBAuEC4QRBBEEEYQRhAzEDMQEBAQENQP1A+ND40PSg9KDwEPAQ+0DrQOZA5kDgAOAA6EDYQN9wz3DE8MTwyPC48Luwq7Cs0JzQnJCMkItwe3B54GngaGBYYFewR7BH0DfQOHAocCkgGSAZYAlgCN/43/eP54/mX9Zf1c/Fz8ZPtk+4T6hPqt+a35zfjN+N/33/fW9tb2q/Wr9XH0cfQq8yrz1fHV8YrwivBJ70nvFO4U7gbtBu0i7CLsXetd68XqxepG6kbqyOnI6VXpVenh6OHoX+hf6Onn6ed753vnEOcQ58Pmw+aN5o3mYeZh5k7mTuY/5j/mIeYh5gLmAube5d7lreWt5ZDlkOWI5YjlkeWR5cblxuUm5ibmqOao5l/nX+c96D3oLOks6S3qLeos6yzrE+wT7O3s7ey47bjtde517jvvO+8W8BbwBPEE8RLyEvI28zbzX/Rf9In1ifWp9qn2uve697/4v/jB+cH5xPrE+tD70Pvx/PH8L/4v/ov/i/8NAQ0BrQKtAlYEVgT5BfkFhgeGB+cI5wgeCh4KNgs2CzAMMAwhDSENGw4bDhYPFg8aEBoQLBEsETQSNBI0EzQTMBQwFBYVFhXxFfEVzhbOFp0XnRdtGG0YTBlMGSgaKBoLGwsbARwBHOcc5xy8HbwdgR6BHhIfEh92H3YfwB/AH98f3x/oH+gf8x/zH+0f7R/kH+Qf6R/pH+Ef4R/UH9Qf0B/QH7gfuB+WH5YfeR95H0ofSh8THxMf5B7kHqMeox5YHlgeER4RHrUdtR1IHUgd0BzQHDQcNBxzG3MbnRqdGqUZpRmaGJoYlBeUF4gWiBZ/FX8VgRSBFH4TfhN5EnkSehF6EXkQeRB5D3kPgQ6BDogNiA2NDI0MlAuUC5cKlwqQCZAJgwiDCGcHZwc7BjsG/gT+BLIDsgNVAlUC7gDuAIT/hP8e/h7+wfzB/Hz7fPtL+kv6K/kr+R/4H/gd9x33HvYe9ir1KvU/9D/0V/NX84DygPKz8bPx5/Dn8CnwKfBu727vqu6q7uvt6+0i7SLtR+xH7GzrbOuO6o7qrOms6d/o3+gp6CnogueC5wLnAuej5qPmWOZY5jHmMeYf5h/mC+YL5gLmAub45fjl3+Xf5dPl0+XT5dPl1+XX5fXl9eUj5iPmT+ZP5ojmiObE5sTm9ub25jDnMOds52znoOeg5+Ln4ucy6DLojeiN6AzpDOms6azpY+pj6jrrOush7CHsBe0F7ent6e3C7sLug++D7zrwOvDo8Ojwi/GL8TbyNvLt8u3yrfOt8330ffRa9Vr1OfY59hv3G/f89/z31fjV+Kn5qfl2+nb6Pfs9+wH8AfzI/Mj8lP2U/WX+Zf44/zj/AwADALoAugBaAVoB4AHgAVECUQK+Ar4CPgM+A9sD2wOoBKgEqwWrBdMG0wYQCBAISQlJCVUKVQobCxsLjwuPC6MLowtpC2kLAAsAC4AKgAoSChIK3AncCeoJ6glMCkwKBAsEC/UL9QsJDQkNIQ4hDgwPDA+vD68PARABEPcP9w+iD6IPJA8kD4sOiw7tDe0NXQ1dDdYM1gxdDF0M/Qv9C68Lrwt1C3ULUAtQCyoLKgv7CvsKwwrDCnwKfAoyCjIK8gnyCbUJtQlyCXIJHwkfCaUIpQj/B/8HMwczB0kGSQZQBVAFWARYBGwDbAOYApgC6QHpAWUBZQEMAQwB2ADYALcAtwCSAJIAUgBSAOj/6P9J/0n/dv52/n/9f/11/HX8c/tz+5T6lPro+ej5c/lz+TP5M/kV+RX5A/kD+fL48vjS+NL4mPiY+EX4RfjY99j3UPdQ98D2wPY19jX2t/W39Vv1W/Uh9SH1AfUB9fz0/PQG9Qb1EfUR9SH1IfUv9S/1NvU29T/1P/VI9Uj1UPVQ9WD1YPV69Xr1mPWY9cP1w/X59fn1NPY09nf2d/a+9r72//b/9kD3QPeH94f32PfY90L4QvjL+Mv4bflt+Sb6Jvrp+un6q/ur+2P8Y/wL/Qv9nv2e/Rr+Gv6B/oH+2v7a/jD/MP+T/5P/DgAOAKQApABYAVgBJwInAgYDBgPwA/AD3gTeBL4FvgWKBooGPgc+B9MH0wdVCFUI0AjQCEYJRgm/Cb8JPQo9CrYKtgorCysLowujCxUMFQyCDIIM7QztDEcNRw2VDZUN4Q3hDSUOJQ5sDmwOwQ7BDhYPFg9rD2sPxQ/FDxIQEhBVEFUQkBCQELAQsBC2ELYQpxCnEHsQexA9ED0Q/Q/9D7MPsw9oD2gPIw8jD9oO2g6PDo8OSQ5JDvsN+w2lDaUNSw1LDeUM5Qx2DHYMBwwHDJELkQsUCxQLkQqRCv4J/glZCVkJowijCNcH1wfyBvIG9gX2BecE5wTIA8gDpQKlAocBhwFyAHIAZv9m/2b+Zv5r/Wv9dPx0/IL7gvuQ+pD6lfmV+Zj4mPiX95f3lfaV9qb1pvXI9Mj08vPy8yfzJ/NO8k7yVfFV8UbwRvAd7x3v3O3c7aDsoOxt623rROpE6kTpROls6Gzotee15zPnM+fW5tbmiOaI5lPmU+Yi5iLm4+Xj5aXlpeVh5WHlD+UP5czkzOSa5JrkdOR05HPkc+SK5IrkpOSk5M7kzuT75PvkHeUd5U3lTeWH5Yflw+XD5RrmGuaL5ovmFOcU58/nz+e76LvoyOnI6fvq++o/7D/see157anuqe7F78XvxPDE8LXxtfGf8p/yiPOI84D0gPSP9Y/1s/az9ur36vcu+S75cvpy+rL7svvt/O38I/4j/lX/Vf+LAIsAyQHJARIDEgNwBHAE7AXsBXoHegcYCRgJuQq5Cj8MPwygDaAN2w7bDuQP5A/JEMkQnxGfEWQSZBIqEyoTAhQCFNoU2hS+Fb4VtBa0FqMXoxeRGJEYhRmFGWEaYRouGy4b+Bv4G6kcqRxRHVEdAx4DHqYeph5AH0Af2R/ZH0wgTCCdIJ0g1yDXIOEg4SDKIMogqiCqIG0gbSAlICUg6B/oH6Mfox9iH2IfNR81HwEfAR/LHsseoB6gHmQeZB4dHh0e2B3YHXodeh0JHQkdjxyPHPkb+RtNG00bmBqYGsoZyhnlGOUY9xf3F/EW8RbbFdsVxhTGFKcTpxOHEocScBFwEVgQWBBDD0MPOA44Di0NLQ0kDCQMIgsiCyMKIwonCScJLwgvCDMHMwcpBikGEAUQBeMD4wOiAqICUwFTAf3//f+i/qL+Qv1C/eb75vuO+o76P/k/+Qn4Cfjq9ur23PXc9eP04/Tx8/Hz/PL88g7yDvIl8SXxO/A78GTvZO+Z7pnu0u3S7SHtIe1+7H7s2+vb60frR+u26rbqGOoY6oHpgeno6OjoR+hH6LTntOcs5yznp+an5kHmQeb45fjlxuXG5b7lvuXU5dTl8eXx5SLmIuZX5lfmgeaB5rXmtebw5vDmJ+cn52/nb+fC58LnFegV6Hfod+jh6OHoROlE6bDpsOke6h7qh+qH6vvq++p563nr+ev5643sjew27Tbt7+3v7cbuxu6077Tvq/Cr8KjxqPGg8qDyhvOG82T0ZPQ79Tv1CvYK9tj22Paj96P3Zvhm+CT5JPnh+eH5nvqe+mH7Yfsr/Cv8+Pz4/Mb9xv2V/pX+Zf9l/zUANQAKAQoB4wHjAb0CvQKZA5kDdQR1BEcFRwUKBgoGuAa4BkUHRQeyB7IHBggGCD0IPQhkCGQIhQiFCKQIpAjRCNEIIQkhCZcJlwk/Cj8KGAsYCwUMBQzwDPAMwA3ADU0OTQ6IDogOdQ51DhIOEg55DXkN0QzRDC8MLwyvC68LaAtoC1ILUgttC20LswuzCw0MDQxuDG4MzAzMDAoNCg0bDRsN/Qz9DKoMqgwxDDEMqguqCxsLGwuNCo0KBQoFCnYJdgngCOAIRghGCKsHqwcXBxcHjgaOBg0GDQaQBZAFGQUZBaUEpQQ4BDgE2gPaA4UDhQMyAzID0QLRAlECUQKlAaUByQDJAMX/xf+o/qj+if2J/YH8gfyj+6P7+fr5+or6ivpQ+lD6Ofo5+jj6OPoy+jL6DfoN+r75vvk4+Tj5ffh9+KP3o/e/9r/25PXk9S/1L/Wl9KX0RPRE9BH0EfT+8/7z/fP98w70DvQj9CP0M/Qz9EH0QfRM9Ez0UPRQ9Fj0WPRk9GT0b/Rv9IH0gfSV9JX0o/Sj9LT0tPTI9Mj03fTd9AH1AfU19TX1evV69dj12PVR9lH23vbe9oL3gvc1+DX47fjt+KP5o/lM+kz63vre+lb7Vvu2+7b7BPwE/Ev8S/ya/Jr8+/z7/Hf9d/0V/hX+1/7X/rf/t/+tAK0AqwGrAZ4CngJ7A3sDPAQ8BN4E3gRqBWoF7AXsBWgGaAboBugGcwdzBwAIAAiTCJMILgkuCcUJxQlYClgK6wrrCnILcgv0C/QLeQx5DPkM+Qx7DXsNAQ4BDnwOfA7rDusOUw9TD6YPpg/mD+YPGRAZEDIQMhA2EDYQLhAuEBUQFRD1D/UP3g/eD8cPxw+0D7QPqw+rD54Png+RD5EPiA+ID3UPdQ9WD1YPKw8rD+QO5A6FDoUOGA4YDpsNmw0YDRgNlgyWDA4MDgyAC4AL7wrvClYKVgq5CbkJHAkcCXgIeAjOB84HIAcgB3EGcQbIBcgFMAUwBaYEpgQlBCUEogOiAxEDEQNnAmcCpQGlAc8AzwDo/+j/8v7y/vP98/3r/Ov83/vf+9/63/rv+e/5EPkQ+UX4RfiD94P3vPa89vf19/Uu9S71XvRe9JTzlPPI8sjy7vHu8RTxFPE28DbwVu9W743uje7a7drtNO007afsp+wj7CPsm+ub6x3rHeuf6p/qFOoU6ovpi+n36PfoUOhQ6LHnsecg5yDnn+af5k3mTeYj5iPmEeYR5iHmIeZG5kbmbeZt5qfmp+bu5u7mMucy54Xnhefc59znLOgs6IjoiOjt6O3oT+lP6cHpwek36jfqpOqk6hbrFuuK64rr/uv+64nsiewx7THt8u3y7dru2u7m7+bvDPEM8U/yT/Kn86fzA/UD9Vv2W/ai96L3yPjI+NH50fnB+sH6o/uj+4P8g/xu/W79aP5o/nD/cP+LAIsAuQG5AfMC8wI5BDkEhgWGBckGyQYACAAILgkuCUsKSwpnC2cLjgyODLUNtQ3hDuEOExATEDIRMhFDEkMSTRNNEzsUOxQWFRYV5RXlFZUWlRYxFzEXzBfMF1sYWxjtGO0YkhmSGTAaMBrMGswabRttG/Yb9htwHHAc6BzoHEcdRx2YHZgd6R3pHSUeJR5WHlYejR6NHrAesB7CHsIezR7NHrQetB5+Hn4eOx47Htgd2B1iHWId7BzsHGQcZBzXG9cbVRtVG88azxpOGk4a4RnhGW8Zbxn6GPoYhxiHGP8X/xdjF2MXuxa7FvcV9xUcFRwVORQ5FEITQhM+Ej4SNhE2ESEQIRAEDwQP6Q3pDc0MzQy2C7YLqgqqCqQJpAmiCKIIpAekB6kGqQawBbAFvwS/BNID0gPmAuYC9gH2Af4A/gD8//z/8v7y/uf95/3a/Nr8yfvJ+7v6u/qq+ar5lviW+I33jfeM9oz2lfWV9bP0s/Tg8+DzF/MX82LyYvK68brxFvEW8YDwgPDv7+/vV+9X78nuye4/7j/utO207TvtO+3N7M3sYuxi7AvsC+y/67/rdOt06zbrNuv76vvqteq16nXqdeo36jfq+On46dDp0Om+6b7puem56dTp1OkG6gbqPuo+6ovqi+rg6uDqL+sv64brhuve697rLewt7ILsguzc7NzsMu0y7ZTtlO0B7gHucO5w7vDu8O5473jv/+//74vwi/AX8Rfxn/Gf8TDyMPLN8s3yc/Nz8yr0KvTr9Ov0r/Wv9Xj2ePZD90P3CvgK+M/4z/iN+Y35P/o/+ub65vqC+4L7FvwW/Kr8qvxD/UP95P3k/Yr+iv4z/zP/2f/Z/3YAdgAJAQkBlQGVARoCGgKgAqACKQMpA7UDtQNFBEUE2ATYBGYFZgXtBe0FbQZtBt4G3gZBB0EHlweXB9oH2gcLCAsIMAgwCEQIRAhMCEwITghOCEcIRwg8CDwINgg2CDQINAhDCEMIcghyCL8IvwgwCTAJwQnBCVkKWQrhCuEKQAtAC1oLWgsjCyMLoQqhCt0J3QnyCPIIAggCCCQHJAdzBnMGAgYCBtIF0gXhBeEFJgYmBoYGhgbpBukGOAc4B1kHWQdAB0AH7wbvBm4GbgbLBcsFGwUbBWUEZQSwA7AD/QL9AkwCTAKiAaIBBQEFAXkAeQD/////kP+Q/yj/KP/C/sL+Yv5i/hP+E/7e/d79wv3C/bj9uP2s/az9iv2K/Uj9SP3f/N/8UPxQ/Kb7pvvl+uX6FPoU+kT5RPmF+IX46ffp94b3hvdh92H3bvdu96L3ovfj9+P3F/gX+Df4N/g7+Dv4Ifgh+Pb39vfA98D3h/eH9173XvdI90j3Q/dD91L3Uvds92z3hPeE9533nfe197X3yPfI9+H34fcB+AH4KPgo+F74Xvin+Kf4AvkC+XP5c/n0+fT5ePp4+vn6+fpx+3H73fvd+0T8RPyq/Kr8D/0P/Xb9dv3d/d39QP5A/qP+o/4M/wz/f/9///7//v+IAIgAFgEWAZwBnAEWAhYCgwKDAuQC5AJCA0IDqQOpAxsEGwSfBJ8ENwU3BdsF2wWHBocGOAc4B9wH3AduCG4I6wjrCEYJRgmFCYUJsQmxCc4JzgnoCegJDgoOCjwKPAp5CnkKyArIChsLGwtwC3ALxwvHCw8MDwxKDEoMfQx9DJ0MnQyzDLMMxQzFDMoMygzHDMcMwgzCDLAMsAyWDJYMeAx4DE8MTwwaDBoM3gveC5ILkgs5CzkL3greCn4KfgogCiAKzgnOCYAJgAk0CTQJ6QjpCJcIlwg6CDoI1QfVB2MHYwfkBuQGXAZcBskFyQUvBS8FlgSWBAUEBQR+A34DBwMHA5oCmgIuAi4CvAG8AT0BPQGtAK0ADgAOAGb/Zv+7/rv+E/4T/nf9d/3n/Of8Yfxh/Oj76Pt4+3j7DfsN+6f6p/pD+kP62fnZ+W35bfn5+Pn4efh5+Pb39vdz93P38fbx9n72fvYV9hX2rfWt9Ur1SvXi9OL0b/Rv9Pzz/POJ84nzFPMU86zyrPJN8k3y8/Hz8avxq/Fx8XHxPfE98RrxGvH68PrwzvDO8J/wn/Bj8GPwE/AT8MPvw+9y73LvIe8h7+Tu5O687rzuou6i7qTupO637rfuze7N7uzu7O4K7wrvHe8d7zHvMe9C70LvSu9K71nvWe9w73Dvj++P78nvye8e8B7whfCF8AXxBfGS8ZLxHPIc8qjyqPIy8zLzs/Oz8zX0NfS19LX0L/Uv9a71rvUy9jL2vfa99lb3Vvf99/33q/ir+GP5Y/kk+iT67vru+sX7xfus/Kz8oP2g/Zz+nP6c/5z/mQCZAI0BjQF0AnQCTwNPAxUEFQTKBMoEcwVzBQ8GDwarBqsGUwdTBwYIBgjKCMoIogmiCX0KfQpbC1sLPgw+DBcNFw3qDeoNvQ69Dn4Pfg8xEDEQ2hDaEGgRaBHlEeURXBJcEsMSwxIlEyUTixOLE+cT5xM+FD4UnRSdFPEU8RRDFUMVnRWdFegV6BUqFioWaRZpFpIWkhasFqwWwhbCFscWxxbCFsIWwxbDFrkWuRaqFqoWnxafFoUWhRZeFl4WMhYyFvAV8BWdFZ0VRxVHFd8U3xRtFG0U/hP+E4QThBMIEwgTlRKVEh0SHRKlEaURMhEyEbUQtRAwEDAQqQ+pDxcPFw9+Dn4O5g3mDUcNRw2jDKMM/Qv9C00LTQuTCpMK0wnTCQsJCwk/CD8IdAd0B6oGqgbhBeEFHQUdBWAEYASrA6sDAwMDA2cCZwLRAdEBPgE+AaYApgAGAAYAW/9b/6r+qv71/fX9O/07/YT8hPzM+8z7FvsW+2r6avrJ+cn5M/kz+av4q/gp+Cn4pPek9yD3IPea9pr2EfYR9pL1kvUc9Rz1qvSq9EX0RfTp8+nzj/OP80PzQ/P+8v7yt/K38nXydfIu8i7y3vHe8ZTxlPFS8VLxFPEU8erw6vDN8M3ws/Cz8KXwpfCf8J/wmfCZ8KDwoPCu8K7wufC58MrwyvDe8N7w7vDu8AvxC/E08TTxY/Fj8aHxofHp8enxL/Iv8nzyfPLK8sryFPMU82LzYvOx87Hz+PP480L0QvSO9I702fTZ9C31LfWL9Yv17fXt9Vf2V/bF9sX2Mfcx96L3ovcW+Bb4iviK+AL5Avl5+Xn56/nr+Vj6WPrB+sH6Ivsi+3/7f/va+9r7Mfwx/Ib8hvzb/Nv8MP0w/Yj9iP3k/eT9Qv5C/qH+of79/v3+Vv9W/6n/qf/5//n/SABIAJQAlADhAOEALQEtAXUBdQG7AbsB/gH+AT4CPgJ5AnkCsAKwAt8C3wIHAwcDKQMpA0QDRANZA1kDaANoA20DbQNkA2QDTgNOAyoDKgMAAwAD2gLaAsACwAK/Ar8C4ALgAiEDIQODA4MD/gP+A4AEgAT0BPQERwVHBWIFYgU4BTgFzATMBCkEKQRjA2MDmAKYAuAB4AFSAVIB/wD/AOwA7AAaARoBgQGBAQ0CDQKoAqgCOQM5A6UDpQPdA90D2gPaA6ADoAM9Az0DxALEAkICQgLCAcIBSQFJAdoA2gB3AHcAJAAkAOT/5P+5/7n/of+h/5b/lv+T/5P/mP+Y/6j/qP/H/8f/8//z/ycAJwBRAFEAXwBfAEMAQwD4//j/gf+B//D+8P5S/lL+tv22/Sz9LP2//L/8ePx4/GH8Yfx8/Hz8wPzA/B/9H/2F/YX93P3c/Rf+F/4w/jD+J/4n/gT+BP7N/c39iv2K/Un9Sf0T/RP98fzx/Ov86/z7/Pv8Gv0a/T39Pf1e/V79e/17/Zz9nP3H/cf9/f39/Tv+O/58/nz+uP64/vH+8f4q/yr/Zf9l/6P/o//b/9v/BQAFABwAHAAlACUALgAuAEQARABzAHMAugC6AA8BDwFmAWYBtgG2Af0B/QFAAkACgwKDAsECwQL2AvYCHgMeAzMDMwM/Az8DUANQA3EDcQOpA6kD+QP5A1MEUwSwBLAECQUJBVUFVQWUBZQFxwXHBecF5wX2BfYF9wX3BeoF6gXZBdkF0AXQBc4FzgXXBdcF7AXsBQUGBQYgBiAGPwY/BloGWgZyBnIGhgaGBpEGkQaTBpMGkgaSBogGiAZ5BnkGZQZlBkUGRQYaBhoG5gXmBasFqwVtBW0FNAU0BfsE+wTDBMMEjASMBFUEVQQhBCEE9gP2A9ID0gOzA7MDlgOWA3IDcgNEA0QDCgMKA8ICwgJuAm4CEAIQAqoBqgE/AT8B1ADUAHAAcAAWABYAyv/K/4r/iv9T/1P/IP8g/+/+7/69/r3+if6J/lX+Vf4f/h/+6v3q/bf9t/2G/Yb9Vf1V/Sb9Jv3z/PP8uPy4/Hn8efw0/DT86vvq+6X7pftl+2X7Lvsu+wb7Bvvq+ur61frV+sr6yvrA+sD6s/qz+qb6pvqW+pb6g/qD+nL6cvpg+mD6R/pH+ij6KPr/+f/5yfnJ+Y35jflM+Uz5BfkF+cT4xPiM+Iz4YPhg+En4SfhF+EX4TfhN+F74Xvhq+Gr4ZPhk+E34Tfgi+CL44ffh95X3lfdA90D35fbl9pT2lPZU9lT2J/Yn9hz2HPYs9iz2SvZK9nD2cPaQ9pD2nvae9qD2oPaU9pT2d/Z39lT2VPYo9ij28fXx9b31vfWU9ZT1evV69X31ffWb9Zv1yvXK9Qn2CfZS9lL2nfad9vH28fZL90v3o/ej9/v3+/dO+E74l/iX+N/43/gm+Sb5avlq+a/5r/nw+fD5Lvou+nD6cPrA+sD6Ivsi+537nfsw/DD81PzU/IL9gv03/jf+8v7y/q7/rv9oAGgAGwEbAbsBuwFJAkkCyALIAj8DPwO7A7sDRwRHBN4E3gR/BX8FJgYmBsgGyAZnB2cHCwgLCKwIrAhOCU4J8gnyCYgKiAoVCxULoAugCyMMIwyoDKgMNg02DcANwA1FDkUOxw7HDjgPOA+eD54PAxADEFsQWxCsEKwQ/RD9ED8RPxF6EXoRtxG3EeoR6hEbEhsSUBJQEnoSehKdEp0SwhLCEtsS2xLvEu8SBxMHExETERMRExETEBMQE/8S/xLmEuYSzxLPEqoSqhJ6EnoSRxJHEgISAhKzEbMRZxFnERIREhG8ELwQbBBsEBQQFBC4D7gPXw9fD/4O/g6aDpoOOg46DtIN0g1kDWQN9Az0DHcMdwzxC/ELagtqC98K3wpSClIKzAnMCUIJQgm1CLUIJQglCI4HjgfyBvIGWAZYBr4FvgUkBSQFjASMBO8D7wNNA00DqgKqAgoCCgJuAW4B2QDZAEgASAC3/7f/Iv8i/4v+i/7y/fL9V/1X/cD8wPwp/Cn8kPuQ+/n6+fpk+mT6zvnO+UD5QPm2+Lb4LPgs+Kj3qPcm9yb3ofah9iP2I/an9af1J/Un9a70rvQ49Dj0xPPE82HzYfMK8wrzuPK48nPyc/Iw8jDy6PHo8aXxpfFk8WTxIfEh8efw5/Cw8LDwcvBy8D3wPfAN8A3w3u/e78DvwO+t763vnO+c75fvl++Z75nvme+Z76Xvpe+677rvzu/O7+7v7u8T8BPwNPA08F7wXvCM8Izwt/C38Orw6vAh8SHxVvFW8ZbxlvHe8d7xJvIm8njyePLP8s/yI/Mj84DzgPPi8+LzRfRF9LH0sfQg9SD1jPWM9fz1/PVt9m323Pbc9lH3UffK98r3QvhC+Lz4vPg3+Tf5r/mv+Sj6KPqh+qH6GfsZ+5D7kPsH/Af8ffx9/PL88vxo/Wj93v3e/VP+U/7I/sj+Pf89/7L/sv8nACcAnACcAA0BDQF6AXoB4AHgATwCPAKOAo4C1wLXAhADEAM8AzwDXANcA3EDcQOFA4UDpQOlA9YD1gMlBCUEmASYBCcFJwXNBc0FfAZ8Bh0HHQefB58H9wf3BxUIFQj6B/oHsgeyB0QHRAfGBsYGTgZOBu0F7QWzBbMFsQWxBeIF4gVHBkcG0wbTBm4HbgcCCAIIfQh9CMoIygjjCOMIzgjOCI0IjQgvCC8IwwfDB04HTgfaBtoGbgZuBggGCAauBa4FZAVkBScFJwX4BPgE1wTXBLsEuwSmBKYEmgSaBJYElgScBJwEqQSpBLAEsASiBKIEcwRzBBsEGwSbA5sD/gL+AlECUQKhAaEB+QD5AGAAYADe/97/eP94/zT/NP8S/xL/C/8L/xb/Fv8j/yP/J/8n/x7/Hv8I/wj/6f7p/sT+xP6Z/pn+Zf5l/in+Kf7j/eP9l/2X/U39Tf0I/Qj9xPzE/IP8g/xB/EH8//v/+8r7yvup+6n7o/uj+7r7uvvn++f7Hvwe/Fj8WPyR/JH8xvzG/Pr8+vwo/Sj9Sv1K/V39Xf1e/V79Uv1S/Uj9SP1J/Un9WP1Y/XT9dP2T/ZP9q/2r/bn9uf3D/cP9zP3M/d/93/39/f39KP4o/l7+Xv6g/qD+8v7y/lT/VP/H/8f/RQBFAMIAwgA0ATQBkQGRAdMB0wH7AfsBDgIOAhACEAIJAgkCAQIBAvwB/AEBAgECFgIWAjkCOQJtAm0CsgKyAgADAANXA1cDtAO0Aw8EDwRoBGgEvwS/BA0FDQVRBVEFiQWJBawFrAW8BbwFuwW7BagFqAWIBYgFYQVhBTIFMgUDBQMF3QTdBMQExAS+BL4EzwTPBO0E7QQTBRMFOQU5BVcFVwVsBWwFegV6BX0FfQVzBXMFWQVZBSoFKgXoBOgEmwSbBEkESQT4A/gDqgOqA1wDXAMMAwwDuwK7AmwCbAIiAiIC4wHjAa4BrgGCAYIBXgFeAT8BPwEkASQBDQENAfcA9wDcANwAuQC5AIkAiQBNAE0ABgAGALf/t/9j/2P/C/8L/7H+sf5X/lf+/v3+/az9rP1i/WL9I/0j/e/87/zE/MT8n/yf/IH8gfxp/Gn8U/xT/EP8Q/w0/DT8H/wf/AT8BPzd+937qfup+277bvsu+y776vrq+qj6qPph+mH6EvoS+r35vflk+WT5CfkJ+br4uvh6+Hr4SPhI+Cn4KfgW+Bb4BvgG+P73/vf29/b36Pfo99f31/e697r3iPeI90T3RPfr9uv2ffZ99g32Dfal9aX1TvVO9Rn1GfUG9Qb1CfUJ9SD1IPU99T31UfVR9WT1ZPVw9XD1cfVx9Wv1a/VW9Vb1K/Ur9fn0+fTK9Mr0qvSq9Kz0rPTR9NH0C/UL9Vb1VvWl9aX17vXu9Tf2N/aB9oH2x/bH9g73DvdS91L3j/eP9833zfcT+BP4Yfhh+L/4v/gp+Sn5l/mX+Qz6DPqG+ob6BvsG+477jvsg/CD8u/y7/F/9X/0O/g7+yf7J/o3/jf9XAFcAHgEeAdcB1wF/An8CGAMYA6MDowMuBC4ExATEBGQFZAUTBhMG1AbUBpwHnAduCG4ITAlMCSoKKgoHCwcL3wvfC6AMoAxIDUgN3w3fDV4OXg7UDtQOUw9TD9IP0g9YEFgQ6hDqEHQRdBH6EfoRgxKDEv4S/hJyE3IT5BPkE0EUQRSPFI8U1xTXFA4VDhU+FT4VdRV1FaEVoRXHFccV7xXvFQQWBBYPFg8WGhYaFhQWFBYEFgQW9RX1FdUV1RWtFa0ViRWJFVgVWBUeFR4V4xTjFJQUlBQ0FDQU0RPRE2ETYRPrEusSfBJ8EgUSBRKIEYgREBEQEY4QjhAKEAoQig+KDwIPAg9wDnAO2A3YDS4NLg11DHUMtAu0C+kK6QoZChkKSQlJCXcIdwihB6EHzgbOBvoF+gUoBSgFXARcBJYDlgPTAtMCDwIPAkkBSQF7AHsApf+l/87+zv72/fb9H/0f/VD8UPyD+4P7tvq2+u757vko+Sj5Y/hj+Kj3qPfy9vL2O/Y79oz1jPXg9OD0M/Qz9JPzk/P58vnyX/Jf8tDx0PFE8UTxtPC08DDwMPCy77LvM+8z78Huwe5Y7lju7e3t7ZXtle1K7UrtBu0G7dbs1uyx7LHsieyJ7GrsauxN7E3sJuwm7AjsCOzu6+7rz+vP67vru+uu667rn+uf66Lrouu067Trz+vP6wHsAexC7ELsheyF7NXs1ewq7Srte+177dft1+057jnume6Z7gDvAO9o72jvxu/G7yfwJ/CK8Irw7PDs8FnxWfHQ8dDxSfJJ8s3yzfJa81rz6fPp84P0g/Qn9Sf1zfXN9Xn2efYp9yn31ffV94D4gPgp+Sn5y/nL+Wv6a/oK+wr7p/un+0X8Rfzk/OT8gf2B/Rv+G/6z/rP+Sf9J/97/3v90AHQADAEMAaIBogE1AjUCxQLFAk4DTgPTA9MDWARYBNcE1wRUBVQF0QXRBUYGRga3BrcGKQcpB5MHkwf3B/cHVQhVCKIIogjdCN0IBwkHCRoJGgkeCR4JHgkeCR0JHQkqCSoJVQlVCZkJmQn6CfoJdQp1CvAK8ApfC18Ltgu2C+EL4QvcC9wLrwuvC1kLWQvrCusKegp6Cg8KDwq5CbkJhwmHCXIJcgl3CXcJkgmSCawJrAm+Cb4JwwnDCbEJsQmMCYwJWwlbCRsJGwnQCNAIfwh/CCQIJAjEB8QHZgdmBwgHCAeoBqgGRQZFBtgF2AVfBV8F4ATgBGEEYQTsA+wDjAOMAz0DPQP6AvoCuwK7AnkCeQIrAisC0QHRAWgBaAHqAOoAVwBXALH/sf8D/wP/Xf5d/tb91v16/Xr9Sv1K/T79Pv1D/UP9Qv1C/TD9MP0C/QL9t/y3/Fb8Vvzj++P7ZPtk++b65vpy+nL6EPoQ+s35zfmp+an5nvme+af5p/m2+bb5v/m/+cL5wvm++b75tvm2+bT5tPm6+br5x/nH+dr52vnw+fD5AfoB+hH6Efoc+hz6H/of+h/6H/ob+hv6EvoS+hD6EPoZ+hn6Mfox+mL6Yvqo+qj6//r/+mX7ZfvQ+9D7Ofw5/Jn8mfzs/Oz8Lf0t/WD9YP2K/Yr9sf2x/eD94P0Z/hn+Xv5e/q3+rf4C/wL/XP9c/7f/t/8RABEAaQBpALkAuQACAQIBRQFFAYMBgwHBAcEBBgIGAlACUAKhAqEC+AL4Ak4DTgOhA6ED7APsAyoEKgRaBFoEgASABJsEmwSvBK8EwgTCBNIE0gTkBOQE+QT5BA4FDgUmBSYFQAVABVYFVgVpBWkFegV6BYYFhgWQBZAFngWeBasFqwW4BbgFxQXFBcsFywXIBcgFvAW8BaIFogV5BXkFRAVEBQQFBAXABMAEgQSBBEoESgQeBB4E/QP9A+MD4wPLA8sDtQO1A5wDnAN+A34DWANYAyQDJAPfAt8CjwKPAjgCOALiAeIBkwGTAU4BTgERAREB2gDaAKgAqAB5AHkATQBNACMAIwD2//b/wv/C/4j/iP9H/0f/BP8E/8X+xf6L/ov+Vf5V/ib+Jv75/fn9zv3O/aX9pf18/Xz9Uv1S/Sn9Kf0E/QT94/zj/ND80PzJ/Mn8zfzN/Nj82Pzj/OP86fzp/Or86vzj/OP80vzS/Lv8u/ya/Jr8b/xv/EH8QfwS/BL85Pvk+7z7vPuZ+5n7ePt4+1z7XPtG+0b7Nfs1+yz7LPsp+yn7J/sn+yX7Jfsi+yL7HPsc+xj7GPsT+xP7B/sH+/P68/rQ+tD6lPqU+kL6QvrX+df5VvlW+c/4z/hO+E744vfi96L3oveV95X3tve29wH4Afhi+GL4wfjB+BT5FPlK+Ur5WPlY+UH5QfkD+QP5oPig+C74Lvi99733Yfdh9zH3Mfc19zX3Yvdi97T3tPcW+Bb4d/h3+NT41Pgq+Sr5dvl2+b35vfn++f75N/o3+m36bfqm+qb66frp+j37Pfuh+6H7DfwN/Hr8evzh/OH8Pv0+/Zr9mv39/f39cf5x/vn++f6V/5X/QgBCAPcA9wCwAbABZAJkAggDCAOWA5YDDQQNBGkEaQS5BLkEEQURBXoFegUEBgQGtAa0Bn0HfQdZCFkIPQk9CRgKGArhCuEKlguWCyMMIwyKDIoM0QzRDPoM+gwZDRkNSg1KDY0NjQ3uDe4NbA5sDvEO8Q51D3UP+Q/5D2oQahDQENAQMxEzEYMRgxHGEcYRAxIDEi4SLhJRElESeBJ4EpQSlBKkEqQSqBKoEooSihJQElASCBIIErERsRFdEV0RHhEeEewQ7BDLEMsQvhC+ELIQshCnEKcQnBCcEHwQfBBCEEIQ8Q/xD3wPfA/sDuwOTw5PDqcNpw3/DP8MYwxjDMwLzAs7CzsLswqzCigKKAqWCZYJ/gj+CFkIWQimB6YH7wbvBjMGMwZ4BXgFxgTGBBwEHAR4A3gD2QLZAjwCPAKaAZoB9AD0AEgASACW/5b/3f7d/iP+I/5p/Wn9sPyw/P/7//tW+1b7sPqw+hH6Efpw+XD5wvjC+A34DfhO9073h/aH9sz1zPUg9SD1hPSE9AT0BPSX85fzNfM18+Py4/KZ8pnyTPJM8gPyA/K08bTxUvFS8evw6/B88HzwAvAC8JLvku8t7y3vzO7M7oDugO5E7kTuDu4O7u3t7e3a7drtze3N7dLt0u3j7ePt9+337RjuGO5B7kHuZ+5n7pXule7F7sXu7+7v7iDvIO9U71Tvg++D77jvuO/u7+7vHvAe8FbwVvCX8Jfw3fDd8DfxN/Gj8aPxGPIY8pzynPIo8yjzsPOw8zv0O/TD9MP0RvVG9cr1yvVQ9lD20vbS9lX3VffX99f3WPhY+N/43/hv+W/5BfoF+qL6ovo/+z/70/vT+138Xfzf/N/8Xf1d/d393f1h/mH+6P7o/m//b//1//X/fAB8AAYBBgGYAZgBNAI0AtEC0QJsA2wDAAQABIUEhQT+BP4EcQVxBdsF2wVBBkEGpgamBgEHAQdTB1MHnQedB9cH1wcBCAEIIAggCDAIMAg5CDkIRQhFCFQIVAhyCHIIqgiqCPgI+AhhCWEJ5gnmCXMKcwr5CvkKagtqC6sLqwu4C7gLlwuXC0oLSgvgCuAKawprCusJ6wluCW4JAwkDCa4Irgh+CH4IfAh8CJwInAjWCNYIHgkeCVoJWgmBCYEJkwmTCYQJhAlYCVgJFAkUCbQItAg+CD4IvAe8By8HLwefBp8GEQYRBoUFhQX8BPwEegR6BAAEAASQA5ADLAMsA9EC0QJ9An0CLQItAt8B3wGQAZABQAFAAe4A7gCYAJgAPAA8AN7/3v9+/37/H/8f/8X+xf5t/m3+FP4U/rX9tf1L/Uv90vzS/FH8UfzJ+8n7QPtA+8D6wPpI+kj62PnY+XX5dfki+SL54Pjg+L34vfi5+Ln4zvjO+Pv4+/gy+TL5ZPlk+Y/5j/mv+a/5v/m/+cT5xPm4+bj5lfmV+WH5Yfke+R750/jT+JD4kPhd+F34O/g7+DD4MPg8+Dz4Xfhd+Jj4mPjv+O/4XPlc+d/53/lt+m36+Pr4+nr7evvr++v7RvxG/I38jfzA/MD84Pzg/Pb89vwE/QT9Ef0R/SH9If05/Tn9XP1c/Y79jv3R/dH9I/4j/n/+f/7i/uL+Rf9F/6b/pv8EAAQAYQBhALsAuwATARMBZQFlAasBqwHnAecBGgIaAkcCRwJzAnMCnwKfAsgCyALuAu4CEAMQAy0DLQNJA0kDZwNnA4YDhgOpA6kD0APQA/kD+QMmBCYEWQRZBI4EjgTGBMYEAwUDBUAFQAV8BXwFuAW4Be0F7QUYBhgGOwY7BlAGUAZZBlkGWgZaBk0GTQY0BjQGEgYSBuYF5gW2BbYFjAWMBWcFZwVKBUoFOAU4BSkFKQUcBRwFEwUTBQoFCgUDBQMF/QT9BPME8wThBOEExwTHBKIEogRzBHMEPQQ9BP8D/wO4A7gDbgNuAyADIAPWAtYCkgKSAlUCVQIdAh0C5wHnAbMBswGAAYABUQFRASgBKAEGAQYB6QDpAM8AzwC1ALUAnACcAIMAgwBpAGkATwBPADEAMQAPAA8A6P/o/73/vf+Q/5D/Y/9j/zf/N/8N/w3/4/7j/rv+u/6T/pP+a/5r/kT+RP4d/h3+8/3z/cz9zP2o/aj9hv2G/Wn9af1O/U79MP0w/Q39Df3k/OT8tPy0/IL8gvxQ/FD8Hfwd/O/77/vB+8H7lPuU+2n7afs/+z/7E/sT++f65/q0+rT6cvpy+iP6I/rD+cP5UPlQ+dT41PhX+Ff44ffh94X3hfdK90r3M/cz90P3Q/dv92/3pfel99/33/cO+A74Jvgm+Cr4KvgQ+BD40PfQ93X3dfcC9wL3hPaE9hn2GfbO9c71qfWp9bD1sPXV9dX1CPYI9kf2R/aP9o/23fbd9jz3PPen96f3FfgV+IT4hPju+O74TvlO+az5rPkI+gj6Xfpd+qr6qvrp+un6FvsW+zv7O/tg+2D7kvuS++D74PtT/FP87Pzs/KX9pf15/nn+Wv9a/zoAOgARAREB1QHVAX8CfwIRAxEDjwOPA/8D/wNuBG4E6QTpBHUFdQUWBhYGzQbNBogHiAdBCEEI8AjwCIkJiQkOCg4KhgqGCuoK6gpGC0YLpgumCwoMCgx9DH0MCA0IDZkNmQ0qDioOtQ61DiUPJQ+AD4AP1A/UDxkQGRBdEF0QqBCoEOoQ6hAqESoRbxFvEaoRqhHcEdwRBRIFEg8SDxL2EfYRxBHEEXMRcxEXERcRyBDIEIIQghBQEFAQPBA8EDQQNBA5EDkQSRBJEE0QTRBAEEAQHxAfENoP2g93D3cPAQ8BD3UOdQ7fDd8NSQ1JDa8MrwwWDBYMgQuBC+cK5wpFCkUKnwmfCe4I7gg6CDoIjAeMB+UG5QZJBkkGuwW7BTYFNgW0BLQEMwQzBKsDqwMVAxUDbAJsAq8BrwHgAOAACAAIADL/Mv9k/mT+n/2f/er86vw//D/8nvue+wv7C/uC+oL6+fn5+XH5cfnf+N/4Pfg9+Jn3mff19vX2V/ZX9sv1y/VN9U311fTV9Gj0aPQD9AP0nvOe80XzRfPy8vLynPKc8k7yTvIA8gDyr/Gv8WfxZ/Ek8STx3vDe8KHwofBm8GbwJvAm8O/v7++877zvh++H72DvYO9E70TvL+8v7zLvMu9K70rva+9r76DvoO/e797vGfAZ8FrwWvCZ8JnwzvDO8APxA/Ez8TPxWvFa8YbxhvG58bnx7vHu8TDyMPJ+8n7y0/LT8jrzOvOx87HzMfQx9L70vvRS9VL14/Xj9Xb2dvYK9wr3nfed9zL4MvjF+MX4T/lP+dL50vlN+k36w/rD+jf7N/uv+6/7J/wn/KL8ovwi/SL9qP2o/TX+Nf7K/sr+Zf9l/wAAAACZAJkAMAEwAcABwAFKAkoCzwLPAkkDSQO4A7gDHQQdBHIEcgS6BLoE9gT2BCQFJAVKBUoFcQVxBZ4FngXcBdwFMgYyBpsGmwYXBxcHoAegByQIJAidCJ0IBAkECUsJSwl1CXUJiQmJCYYJhgl4CXgJZwlnCU8JTwkyCTIJFQkVCfMI8wjTCNMIvQi9CKwIrAiiCKIIoQihCJ4IngicCJwIoQihCKQIpAinCKcIpQilCJMIkwhuCG4IOAg4CO4H7geRB5EHKgcqB7MGswYvBi8GpAWkBRIFEgWABIAE9wP3A3sDewMSAxIDvQK9AnwCfAJLAksCJQIlAgICAgLeAd4BtAG0AYQBhAFJAUkBAwEDAbQAtABaAFoA+f/5/5P/k/8k/yT/rP6s/in+Kf6d/Z39D/0P/Y38jfwh/CH80PvQ+577nvuD+4P7ePt4+3v7e/uJ+4n7m/ub+637rfu0+7T7pful+4T7hPtT+1P7G/sb++j66Pq8+rz6jvqO+mD6YPos+iz68/nz+cL5wvmf+Z/5i/mL+Yv5i/mY+Zj5rfmt+c35zfn2+fb5J/on+mP6Y/qm+qb67Prs+jX7Nft9+337wPvA+/z7/Psv/C/8V/xX/Hj8ePyV/JX8q/yr/L/8v/zT/NP85/zn/AP9A/0s/Sz9Yv1i/aP9o/3r/ev9N/43/ob+hv7c/tz+PP88/6X/pf8TABMAgACAAOcA5wBFAUUBmgGaAeUB5QEjAiMCUAJQAmkCaQJxAnECcgJyAnMCcwJ9An0ClQKVArUCtQLbAtsCCAMIAzgDOANwA3ADsQOxA/UD9QM8BDwEhASEBMgEyAQJBQkFSgVKBYIFggWwBbAF0QXRBeAF4AXdBd0FzwXPBbQFtAWRBZEFbAVsBUYFRgUlBSUFDgUOBf8E/wT5BPkE/gT+BAYFBgURBREFIAUgBS4FLgU5BTkFQgVCBUMFQwU4BTgFJQUlBQUFBQXcBNwErgSuBHkEeQRCBEIECAQIBMoDygOFA4UDOwM7A+wC7AKeAp4CVwJXAhoCGgLoAegBwQHBAZ4BngF+AX4BYQFhAUcBRwEwATABGQEZAf0A/QDYANgAqACoAHEAcQA2ADYA/P/8/8X/xf+P/4//Wf9Z/yH/If/n/uf+q/6r/nP+c/4+/j7+Dv4O/uT95P3B/cH9pf2l/ZD9kP2A/YD9b/1v/V39Xf1D/UP9Hf0d/ez87Pys/Kz8XPxc/AP8A/yj+6P7Q/tD++767vqr+qv6fPp8+mj6aPpm+mb6bPps+nP6c/pr+mv6SfpJ+g76Dvq1+bX5Pfk9+bb4tvgl+CX4lPeU9xn3GffA9sD2jfaN9oj2iPal9qX20fbR9gL3Avcq9yr3P/c/90n3SfdH90f3Mvcy9xH3Effi9uL2qPao9nP2c/ZL9kv2MPYw9iX2JfYi9iL2GvYa9hj2GPYj9iP2Q/ZD9on2ifb19vX2e/d79xX4Ffi5+Ln4WflZ+fL58vl8+nz66/rr+j77Pvt0+3T7k/uT+6z7rPvR+9H7C/wL/Gb8Zvzj/OP8ff19/Sz+LP7o/uj+pv+m/10AXQAHAQcBpQGlATYCNgLCAsICTwNPA+AD4AN6BHoEIwUjBdMF0wWIBogGPgc+B+MH4wd0CHQI9gj2CGIJYgnHCccJMgoyCp4KngoSCxILlQuVCxsMGwykDKQMMA0wDaoNqg0PDg8OYA5gDpYOlg6/Dr8O8g7yDi0PLQ96D3oP3g/eD0cQRxCxELEQHhEeEXURdRG0EbQR2BHYEdAR0BGhEaERYBFgEQ0RDRG+EL4QgxCDEFMQUxAvEC8QGRAZEAAQABDjD+MPxA/ED5MPkw9SD1IPCQ8JD7MOsw5ZDlkOBw4HDrMNsw1eDV4NBw0HDaAMoAwnDCcMngueC/4K/gpHCkcKgwmDCbQItAjpB+kHMgcyB5UGlQYTBhMGqQWpBUkFSQXmBOYEeQR5BPoD+gNjA2MDtgK2AvcB9wEqASoBXABcAJn/mf/o/uj+S/5L/sP9w/1F/UX9yPzI/Ef8R/y9+737J/sn+4v6i/rq+er5R/lH+a74rvgh+CH4n/ef9y33LffF9sX2XPZc9vj1+PWU9ZT1KvUq9cf0x/Rr9Gv0EvQS9MrzyvOQ85DzYvNi80jzSPM58znzKPMo8xjzGPP98v3yzfLN8pbylvJW8lbyDvIO8tTx1PGp8anxjfGN8Y7xjvGo8ajx0PHQ8QvyC/JP8k/yj/KP8tDy0PIN8w3zPfM982zzbPOa85rzxfPF8/rz+vM69Dr0fPR89Mb0xvQQ9RD1UvVS9ZP1k/XT9dP1D/YP9lH2UfaZ9pn25Pbk9jn3OfeY95j3APgA+HT4dPjw+PD4bvlu+ev56/ll+mX62PrY+kj7SPu2+7b7Ivwi/JD8kPz//P/8b/1v/d/93/1N/k3+sv6y/gr/Cv9P/0//f/9//57/nv+z/7P/yv/K/+7/7v8qACoAhwCHAAUBBQGjAaMBVgJWAgsDCwOxA7EDOgQ6BJwEnATZBNkE+gT6BAcFBwUIBQgFAwUDBfIE8gTVBNUEsQSxBIkEiQRpBGkEWwRbBF0EXQRxBHEEkgSSBLkEuQTpBOkEJwUnBW8FbwW/Bb8FDAYMBkUGRQZcBlwGTwZPBh8GHwbXBdcFgwWDBSgFKAXJBMkEagRqBAgECAStA60DYQNhAysDKwMNAw0DAgMCAwADAAP9Av0C9QL1AugC6ALZAtkCzALMAsICwgK3ArcCqgKqApICkgJwAnACQgJCAgoCCgLHAccBegF6ASUBJQHIAMgAaABoAAkACQCz/7P/af9p/y//L/8G/wb/6v7q/tj+2P7J/sn+uf65/qb+pv6Q/pD+eP54/mT+ZP5Z/ln+Vv5W/l3+Xf5n/mf+av5q/mH+Yf5G/kb+Fv4W/tf91/2P/Y/9RP1E/f/8//zE/MT8mPyY/ID8gPyA/ID8lPyU/Lv8u/zw/PD8K/0r/Wn9af2n/af94v3i/R7+Hv5Y/lj+kP6Q/sP+w/7s/uz+Cf8J/xf/F/8W/xb/CP8I//P+8/7d/t3+yv7K/sH+wf7H/sf+2/7b/gH/Af85/zn/gv+C/9n/2f88ADwApQClAAoBCgFoAWgBuwG7AQACAAI2AjYCXQJdAnMCcwJ4AngCcQJxAmACYAJPAk8CQgJCAjwCPAI9Aj0CRQJFAlICUgJjAmMCfAJ8ApoCmgLBAsEC8QLxAiYDJgNfA18DmQOZA88DzwP8A/wDIAQgBDQENAQ5BDkEMAQwBBgEGAT1A/UDzQPNA6ADoANzA3MDSANIAx8DHwP5AvkC1gLWArUCtQKWApYCfQJ9AmwCbAJmAmYCawJrAncCdwKGAoYCkgKSApQClAKIAogCbgJuAkYCRgIQAhAC0gHSAYwBjAFBAUEB9QD1AKoAqgBjAGMAHwAfAN//3/+i/6L/Z/9n/y7/Lv/7/vv+zv7O/qn+qf6N/o3+eP54/mv+a/5i/mL+Xf5d/lj+WP5P/k/+Pf49/iL+Iv78/fz9zv3O/Z79nv1t/W39O/07/Qr9Cv3Z/Nn8qfyp/IL8gvxo/Gj8WfxZ/FX8VfxW/Fb8VPxU/FH8UfxR/FH8VvxW/GX8Zfx+/H78mPyY/LH8sfzB/MH8wvzC/LX8tfyV/JX8Xvxe/BT8FPy9+737Yvti+xb7Fvvk+uT60frR+t/63/oD+wP7L/sv+1n7Wfty+3L7b/tv+1H7UfsY+xj7y/rL+nv6e/o0+jT6APoA+un56fnt+e35BPoE+iv6K/pZ+ln6gfqB+qL6ovq0+rT6sfqx+qD6oPqD+oP6X/pf+kL6Qvoy+jL6Mfox+kT6RPpl+mX6i/qL+rL6svrS+tL65vrm+vf69/oO+w77Nvs2+3v7e/vg++D7Xvxe/Oz87Px9/X39BP4E/nf+d/7P/s/+B/8H/x7/Hv8Y/xj/A/8D/+/+7/70/vT+I/8j/4L/gv8PAA8AvAC8AHUBdQErAisC0wLTAmYDZgPmA+YDVwRXBLkEuQQSBRIFawVrBcgFyAUwBjAGqAaoBiMHIweaB5oHAwgDCFAIUAiFCIUIsAiwCNcI1wgMCQwJWwlbCcEJwQk9Cj0KzgrOCmELYQvsC+wLZQxlDLYMtgzeDN4M6AzoDNcM1wzDDMMMwQzBDNEM0Qz5DPkMOw07DYQNhA3RDdENHg4eDlMOUw5qDmoOXw5fDikOKQ7WDdYNfw1/DSsNKw3rDOsMxgzGDK0MrQyZDJkMhwyHDGkMaQxADEAMEQwRDNUL1QuPC48LSgtKCwILAgvACsAKjQqNCl0KXQoqCioK7gnuCZgJmAkkCSQJlAiUCOkH6QcvBy8HeAZ4Bs0FzQU9BT0F0QTRBIcEhwRZBFkEPQQ9BCIEIgT6A/oDvAO8A2ADYAPlAuUCTwJPAqgBqAH9AP0AXABcAM7/zv9V/1X/6v7q/oj+iP4l/iX+uP24/UX9Rf3K/Mr8SfxJ/Mv7y/tU+1T75/rn+pD6kPpM+kz6FPoU+uT55Pmq+ar5WPlY+fP48/h6+Hr49vf293j3ePcK9wr3rPas9mz2bPZG9kb2NvY29j32PfZO9k72WfZZ9lj2WPY/9j/2CPYI9sL1wvVz9XP1JfUl9er06vTE9MT0r/Sv9LP0s/TJ9Mn05vTm9A71DvU39Tf1WfVZ9Xn1efWW9Zb1q/Wr9cf1x/Xo9ej1DfYN9jv2O/Zq9mr2j/aP9qz2rPa49rj2svay9qX2pfaX9pf2j/aP9pz2nPbE9sT2BPcE92H3YffR99H3RvhG+Ln4ufgc+Rz5Y/lj+Y/5j/mg+aD5lvmW+YH5gflr+Wv5Xvle+Wv5a/mY+Zj55/nn+Vb6VvrY+tj6W/tb+9H70fsx/DH8dfx1/Kj8qPzU/NT8A/0D/Tv9O/17/Xv9uv26/fH98f0b/hv+Nv42/kf+R/5V/lX+ZP5k/nj+eP6X/pf+wv7C/v7+/v5O/07/r/+v/xkAGQB+AH4A0QDRAAoBCgEoASgBMgEyATEBMQEsASwBKQEpASgBKAEtAS0BPAE8AVcBVwF/AX8BtQG1Ae8B7wEnAicCVQJVAnMCcwKBAoECggKCAnoCegJxAnECcAJwAnkCeQKOAo4CrgKuAs8CzwLvAu8CDQMNAyUDJQM9Az0DVwNXA3ADcAOHA4cDnQOdA7ADsAPCA8ID1QPVA+QD5APrA+sD5gPmA9ID0gOyA7IDjQONA2YDZgNEA0QDKwMrAx4DHgMjAyMDPQM9A2gDaAObA5sDzQPNA+wD7APyA/ID5APkA8YDxgOiA6IDgAOAA18DXwNBA0EDJQMlAwoDCgP0AvQC4ALgAssCywKvAq8CiwKLAmICYgI7AjsCIAIgAhQCFAIVAhUCHwIfAikCKQIuAi4CLgIuAiYCJgIXAhcCAQIBAuIB4gG8AbwBkwGTAW4BbgFRAVEBPgE+ATMBMwErASsBIAEgAQ0BDQHwAPAAzADMAKQApAB9AH0AXQBdAEUARQA3ADcANAA0ADsAOwBIAEgAWgBaAGsAawB3AHcAeAB4AG0AbQBXAFcAOwA7ACEAIQAMAAwA/f/9//L/8v/m/+b/2P/Y/8j/yP+5/7n/q/+r/6H/of+X/5f/jv+O/4j/iP+I/4j/lf+V/7H/sf/Z/9n/BQAFACwALABGAEYATgBOAEMAQwAnACcAAAAAANH/0f+g/6D/dP90/1L/Uv8+/z7/Nv82/zb/Nv85/zn/OP84/zT/NP8t/y3/J/8n/yP/I/8j/yP/I/8j/yL/Iv8e/x7/Gf8Z/xT/FP8Q/xD/CP8I//n++f7g/uD+uP64/oj+iP5U/lT+Iv4i/vn9+f3a/dr9wf3B/bD9sP2j/aP9mP2Y/ZP9k/2Q/ZD9jv2O/Y39jf2L/Yv9h/2H/Yf9h/2M/Yz9lv2W/ab9pv24/bj9x/3H/dD90P3O/c79wf3B/av9q/2Q/ZD9c/1z/Vz9XP1Q/VD9Tf1N/Vb9Vv1l/WX9eP14/Y79jv2p/an9yP3I/ev96/0P/g/+Mv4y/lP+U/5z/nP+lv6W/r/+v/7s/uz+Gf8Z/z7/Pv9U/1T/Vf9V/z//P/8T/xP/1P7U/ov+i/5A/kD+Av4C/t793v3d/d39/f39/Tf+N/56/nr+tf61/t7+3v7z/vP++P74/vT+9P7w/vD+8P7w/vb+9v7+/v7+A/8D/wD/AP/y/vL+2f7Z/rj+uP6V/pX+cv5y/k7+Tv4m/ib++P34/cz9zP2s/az9pP2k/bz9vP3x/fH9M/4z/nH+cf6e/p7+tP60/rj+uP61/rX+sv6y/rP+s/68/rz+zf7N/uz+7P4a/xr/Vf9V/5T/lP/J/8n/6f/p//D/8P/j/+P/zP/M/7j/uP+z/7P/xP/E/+7/7v8zADMAjgCOAPYA9gBhAWEBxgHGAR8CHwJuAm4CuwK7AgcDBwNTA1MDnwOfA+YD5gMrBCsEdwR3BMgEyAQdBR0FcQVxBbEFsQXWBdYF5QXlBeMF4wXjBeMF9gX2BR4GHgZgBmAGvAa8BiUHJQeXB5cHCwgLCG0IbQi2CLYI5wjnCAAJAAkRCREJKwkrCUwJTAl0CXQJoQmhCcIJwgnaCdoJ8AnwCQAKAAoMCgwKEwoTCgcKBwrmCeYJvgm+CZIJkglzCXMJbQltCXYJdgmHCYcJmgmaCZ4JngmSCZIJegl6CVIJUgkgCSAJ7QjtCLcItwiFCIUIYghiCEkISQg5CDkILgguCBcIFwjuB+4HrAesB04HTgfZBtkGWwZbBt4F3gVtBW0FEwUTBc0EzQSYBJgEbgRuBEYERgQbBBsE7APsA7UDtQN1A3UDMQMxA+cC5wKcApwCUwJTAg0CDQLKAcoBhwGHAUABQAHvAO8AkQCRACUAJQCq/6r/JP8k/5z+nP4b/hv+qf2p/U/9T/0O/Q793vze/Lf8t/yM/Iz8VPxU/A78Dvy8+7z7Yfth+wb7Bvut+q36V/pX+gr6CvrG+cb5ifmJ+Vn5Wfkv+S/5AfkB+c74zviO+I74Pfg9+Oj36PeV95X3SPdI9xD3EPfp9un2zPbM9rv2u/av9q/2nfad9or2ivZw9nD2S/ZL9ij2KPYK9gr28vXy9e317fX49fj1DPYM9i32LfZO9k72Z/Zn9nj2ePZ79nv2avZq9lD2UPYw9jD2EPYQ9gD2APYC9gL2D/YP9in2KfZD9kP2U/ZT9lz2XPZe9l72WfZZ9ln2WfZj9mP2evZ69qz2rPb49vj2WfdZ98r3yvc9+D34ovii+Pb49vg3+Tf5Y/lj+YH5gfmR+ZH5jvmO+X75fvlp+Wn5V/lX+Vv5W/l5+Xn5q/mr+ej56Pkk+iT6V/pX+on6ifrF+sX6EvsS+3b7dvvu++77bvxu/O/87/xs/Wz94P3g/Uf+R/6b/pv+1f7V/vP+8/76/vr+9f71/vL+8v78/vz+F/8X/0L/Qv98/3z/w//D/xQAFABtAG0AyADIABwBHAFmAWYBqgGqAe0B7QE6AjoCmAKYAgIDAgNxA3ED4APgA0cERwSkBKQE+gT6BD0FPQVpBWkFfAV8BXUFdQVlBWUFXwVfBWsFawWNBY0FvQW9BekF6QULBgsGJQYlBjgGOAZMBkwGZgZmBn8GfwacBpwGxAbEBvkG+QZBB0EHmgeaB+wH7AcpCCkIRghGCD8IPwgfCB8I9Af0B74Hvgd/B38HNwc3B+UG5QaUBpQGVgZWBi0GLQYZBhkGDgYOBvsF+wXYBdgFrQWtBX8FfwVcBVwFTAVMBUcFRwVHBUcFRwVHBT8FPwUuBS4FEgUSBeUE5QShBKEESARIBNwD3ANnA2cD9wL3ApECkQI6AjoC8AHwAa8BrwF0AXQBPQE9AQgBCAHUANQAnwCfAGoAagA1ADUAAgACANX/1f+y/7L/mf+Z/4r/iv+A/4D/c/9z/17/Xv86/zr/BP8E/8D+wP5w/nD+Gf4Z/sP9w/1u/W79Hv0e/df81/yf/J/8dfx1/F38XfxR/FH8RfxF/Df8N/wl/CX8EvwS/Av8C/wW/Bb8NPw0/F/8X/yO/I78tPy0/ND80Pzg/OD85Pzk/N/83/zO/M78r/yv/If8h/xd/F38Nvw2/B38HfwU/BT8GPwY/Cb8Jvw+/D78Xvxe/If8h/y6/Lr87/zv/CL9Iv1Q/VD9df11/Zb9lv23/bf91/3X/fb99v0M/gz+Ff4V/hP+E/4L/gv+Av4C/v/9//0B/gH+Av4C/v/9//32/fb96/3r/eX95f3r/ev9Af4B/if+J/5U/lT+gf6B/qb+pv6//r/+zv7O/tf+1/7k/uT+/P78/iX/Jf9c/1z/l/+X/8n/yf/h/+H/2P/Y/67/rv9w/3D/Mv8y/wj/CP8A/wD/G/8b/03/Tf+E/4T/r/+v/8X/xf/J/8n/yP/I/9D/0P/u/+7/JQAlAG8AbwDBAMEAEQERAVIBUgF9AX0BkAGQAYkBiQFsAWwBQgFCARgBGAH5APkA7wDvAPcA9wAOAQ4BKwErAUQBRAFaAVoBcQFxAYwBjAGyAbIB4gHiARcCFwJKAkoCeQJ5ApwCnAKzArMCwALAAsMCwwK8ArwCrQKtApECkQJiAmICHAIcAsABwAFYAVgB9QD1AKcApwB7AHsAcABwAHwAfACTAJMArACsAMcAxwDpAOkAEwETAT8BPwFgAWABaQFpAVMBUwEkASQB6gDqALQAtACFAIUAVwBXACAAIADV/9X/ev96/xr/Gv/E/sT+gf6B/lX+Vf45/jn+Kf4p/iL+Iv4n/if+Ov46/lz+XP6H/of+r/6v/sv+y/7W/tb+0P7Q/r/+v/6p/qn+k/6T/oP+g/57/nv+fv5+/oz+jP6h/qH+sv6y/rf+t/6p/qn+iv6K/mX+Zf5L/kv+RP5E/lX+Vf52/nb+n/6f/sv+y/7+/v7+Qf9B/5n/mf8BAAEAbQBtAMsAywASARIBRwFHAXUBdQGrAasB8AHwATsCOwJ+An4CrQKtAsICwgLBAsECtwK3Aq4CrgKsAqwCuQK5AtIC0gL/Av8CRANEA54DngMIBAgEfQR9BO4E7gRZBVkFvAW8BRMGEwZfBl8GnwafBs8GzwbzBvMGEwcTBy8HLwdKB0oHZQdlB3QHdAd0B3QHagdqB1gHWAdJB0kHRgdGB0sHSwdYB1gHbgduB4YHhgekB6QHywfLB/EH8QcTCBMILQgtCDYINggvCC8IHggeCAEIAQjdB90HvAe8B5sHmweDB4MHdgd2B2sHawdbB1sHQAdABw8HDwfJBskGdwZ3BhsGGwa+Bb4FaAVoBRgFGAXQBNAElwSXBGcEZwRABEAEHAQcBPED8QO8A7wDgAOAAz8DPwMAAwADxQLFAogCiAJGAkYC/QH9Aa0BrQFZAVkBAAEAAaMAowA4ADgAvP+8/zP/M/+j/qP+Gf4Z/qH9of09/T396/zr/Kb8pvxp/Gn8L/wv/Pv7+/vJ+8n7lfuV+1/7X/sj+yP73frd+pP6k/pF+kX69vn2+az5rPln+Wf5JPkk+ej46Pis+Kz4afhp+CL4IvjS99L3evd69yz3LPfs9uz2vva+9qj2qPaf9p/2kvaS9oD2gPZh9mH2M/Yz9gP2A/bZ9dn1tfW19aP1o/Wg9aD1pPWk9bj1uPXX9df1/PX89Sz2LPZc9lz2evZ69oX2hfZ09nT2RvZG9hD2EPbe9d71uvW69a71rvW29bb1x/XH9eb15vUQ9hD2Q/ZD9on2ifba9tr2LPcs9333fffK98r3EvgS+GD4YPi1+LX4CfkJ+Vr5Wvmf+Z/50vnS+fr5+vkc+hz6OPo4+lX6Vfp1+nX6mPqY+sr6yvoR+xH7a/tr+9X71ftE/ET8rPys/A39Df1q/Wr9yf3J/S/+L/6Y/pj+/f79/ln/Wf+w/7D/CwALAHEAcQDkAOQAXAFcAckByQEiAiICZAJkApUClQLDAsMC+AL4AjcDNwN9A30DyQPJAxIEEgRZBFkEoASgBOME4wQjBSMFYwVjBZ0FnQXXBdcFGQYZBmAGYAauBq4GAwcDB1IHUgeZB5kH2QfZBwoICggvCC8ISghKCFUIVQhPCE8IOwg7CBgIGAjsB+wHxQfFB6MHoweLB4sHfwd/B3QHdAdmB2YHVwdXB0MHQwcwBzAHJAckBxoHGgcRBxEHCwcLBwAHAAfxBvEG4AbgBsAGwAaJBokGOgY6BtAF0AVVBVUF2wTbBGgEaAQDBAMEqQOpA1EDUQP5AvkCpQKlAlkCWQIYAhgC4AHgAakBqQFsAWwBKQEpAeUA5QClAKUAagBqACsAKwDd/93/ff99/xT/FP+y/rL+Zv5m/jj+OP4b/hv++f35/cP9w/1x/XH9Cv0K/aD8oPxA/ED86fvp+5f7l/tA+0D73/rf+oL6gvo9+j36Hvoe+jH6Mfpo+mj6qvqq+t/63/ry+vL63/rf+rz6vPqg+qD6nvqe+sD6wPr0+vT6HPsc+yD7IPvy+vL6nPqc+j76Pvr7+fv57Pns+Rv6G/p7+nv68vry+mn7afvP+8/7Gvwa/E38Tfxv/G/8h/yH/J/8n/y+/L785vzm/B/9H/1r/Wv9yv3K/Tj+OP6o/qj+Bv8G/z//P/9M/0z/Nf81/xX/Ff8N/w3/MP8w/33/ff/a/9r/JgAmAE4ATgBTAFMATABMAFMAUwB0AHQAqQCpAN4A3gAEAQQBHwEfATsBOwFpAWkBrgGuAfkB+QEzAjMCTgJOAkoCSgI4AjgCLQItAjACMAI3AjcCMAIwAgoCCgLGAcYBdgF2ATIBMgETARMBJwEnAWQBZAG3AbcBAgICAikCKQIgAiAC7wHvAa4BrgGCAYIBiQGJAccBxwElAiUCegJ6ApkCmQJpAmkC8AHwAU4BTgGzALMARwBHAB8AHwA7ADsAgwCDANgA2AAeAR4BRgFGAU0BTQE/AT8BLwEvASkBKQEuAS4BNQE1AS8BLwETARMB5ADkALAAsACNAI0AiwCLAKsAqwDkAOQAIAEgAUwBTAFYAVgBRAFEARoBGgHwAPAA3QDdAO4A7gAfAR8BWwFbAYMBgwGHAYcBagFqAUEBQQErASsBOwE7AWkBaQGdAZ0BvQG9Ab0BvQGrAasBoAGgAbIBsgHmAeYBMgIyAn0CfQK0ArQC0ALQAskCyQKhAqECYgJiAhUCFQLTAdMBswGzAcEBwQH2AfYBOQI5AmcCZwJuAm4CTwJPAh8CHwL3AfcB5wHnAekB6QHzAfMB+wH7AfwB/AH3AfcB7gHuAdgB2AGuAa4BcAFwASgBKAHmAOYAuAC4AKEAoQCXAJcAkgCSAI0AjQCLAIsAjwCPAJUAlQCOAI4AbQBtAC8ALwDh/+H/mP+Y/2n/af9Z/1n/Wf9Z/1r/Wv9N/03/M/8z/xT/FP/7/vv+7P7s/uX+5f7h/uH+4P7g/uL+4v7l/uX+5v7m/uH+4f7T/tP+v/6//q3+rf6f/p/+k/6T/oX+hf5v/m/+VP5U/kL+Qv5G/kb+Y/5j/pX+lf7P/s/+A/8D/yz/LP9I/0j/Wv9a/2X/Zf9o/2j/Zv9m/2T/ZP9r/2v/gP+A/6X/pf/V/9X/CAAIADkAOQBpAGkAmwCbAMwAzAD5APkAHwEfATsBOwFWAVYBeAF4AagBqAHiAeIBHwIfAk8CTwJvAm8CfwJ/AoMCgwKEAoQChgKGAowCjAKbApsCvQK9AvAC8AIzAzMDfQN9A7wDvAPmA+YD/AP8A/8D/wP5A/kD9AP0A+4D7gPsA+wD8APwA/oD+gMLBAsEHwQfBCkEKQQiBCIEDAQMBOsD6wPPA88DwgPCA8ADwAPEA8QDxwPHA8MDwwO9A70DuwO7A7gDuAOsA6wDjAOMA04DTgP7AvsCowKjAlcCVwIjAiMCBgIGAvcB9wHoAegB0wHTAbABsAF+AX4BPgE+AfUA9QCoAKgAYQBhACYAJgD4//j/0v/S/63/rf+E/4T/V/9X/yr/Kv///v/+0/7T/qL+ov5l/mX+Hf4d/tP90/2T/ZP9Yf1h/UL9Qv0u/S79Gv0a/QL9Av3f/N/8rfyt/G/8b/wo/Cj84Pvg+6b7pvuD+4P7d/t3+377fvuJ+4n7ivuK+4D7gPtt+237VvtW+0T7RPsy+zL7FfsV++767vq/+r/6kPqQ+nP6c/pt+m36ePp4+o/6j/qj+qP6qPqo+qD6oPqM+oz6b/pv+lb6VvpL+kv6UvpS+nP6c/qn+qf63frd+gr7Cvsj+yP7J/sn+yT7JPsm+yb7M/sz+0z7TPto+2j7gfuB+5f7l/ux+7H71PvU+wL8Avw1/DX8Xvxe/Hf8d/x+/H78efx5/Hj8ePyH/If8qfyp/OH84fwq/Sr9ef15/cf9x/0L/gv+P/4//mX+Zf6B/oH+l/6X/rH+sf7S/tL+9/73/h7/Hv9D/0P/ZP9k/4H/gf+f/5//vv++/93/3f/5//n/EgASACcAJwA7ADsAVQBVAHkAeQCoAKgA4QDhABkBGQFEAUQBXAFcAVoBWgFFAUUBLQEtASABIAErASsBTwFPAYEBgQGxAbEB0AHQAdUB1QHGAcYBsAGwAaMBowGpAakBvQG9AdEB0QHTAdMBtwG3AYABgAE/AT8BDQENAfwA/AAQARABOgE6AV8BXwFpAWkBUAFQASABIAHxAPEA3ADcAOkA6QAMAQwBLgEuATkBOQEhASEB7wDvALUAtQCDAIMAYQBhAEkASQAtAC0ABAAEAM3/zf+U/5T/av9q/13/Xf9y/3L/n/+f/9H/0f/y//L/8v/y/8//z/+U/5T/Wf9Z/zf/N/88/zz/Xf9d/4L/gv+I/4j/Xf9d/wb/Bv+j/qP+Yv5i/mP+Y/6p/qn+FP8U/3X/df+e/57/f/9//yb/Jv+6/rr+Zv5m/kz+TP5y/nL+yP7I/ir/Kv9x/3H/fP98/0f/R//o/uj+kP6Q/nT+dP6w/rD+Nf81/8z/zP8vAC8AMQAxANX/1f9P/0//5P7k/sz+zP4L/wv/ff99/+f/5/8cABwADwAPANP/0/+K/4r/VP9U/0P/Q/9b/1v/lf+V/+X/5f87ADsAhACEAKwArACtAK0AkQCRAGoAagBPAE8ASgBKAFgAWABpAGkAbwBvAGYAZgBRAFEAPQA9ADQANAA6ADoAUwBTAHoAegCpAKkA1gDWAPEA8QDrAOsAvwC/AHYAdgApACkA+f/5/wEAAQBLAEsAyQDJAFEBUQGxAbEBvQG9AWUBZQHBAMEADgAOAJD/kP99/33/2f/Z/3oAegAeAR4BjgGOAbIBsgGYAZgBXgFeARgBGAHOAM4AggCCAEEAQQAuAC4AaABoAPQA9ACqAaoBQQJBAnICcgInAicCiAGIAeUA5QCPAI8ArACsACwBLAHZAdkBeAJ4AtgC2ALmAuYCpAKkAiUCJQKRAZEBHAEcAfoA+gBIAUgB8QHxAbUCtQJCA0IDYgNiAxEDEQOHAocCEQIRAuYB5gEXAhcCiQKJAgsDCwN0A3QDqwOrA6ADoANVA1UD2QLZAk4CTgLmAeYB1QHVATECMQLjAuMCrAOsAzoEOgRYBFgEBAQEBGkDaQPJAskCXQJdAjsCOwJcAlwCqQKpAv8C/wJGA0YDbANsA2cDZwM8AzwD/wL/AskCyQKvAq8CuwK7At0C3QL/Av8CDwMPAwQDBAPnAucCxALEAqMCowKFAoUCagJqAk4CTgItAi0CBwIHAtgB2AGgAaABagFqAUUBRQFGAUYBcwFzAbsBuwH9Af0BFwIXAvoB+gGxAbEBWAFYAQgBCAHKAMoAlgCWAGIAYgAvAC8ACQAJAAIAAgAZABkAPAA8AFAAUABCAEIAFgAWAOD/4P+3/7f/oP+g/5P/k/9//3//X/9f/z3/Pf8q/yr/K/8r/zf/N/83/zf/Gv8a/+P+4/6k/qT+dP50/l7+Xv5e/l7+Zf5l/nD+cP6C/oL+pf6l/tr+2v4N/w3/Hf8d//X+9f6T/pP+Ev4S/qL9ov1n/Wf9cP1w/a/9r/0G/gb+Uv5S/n3+ff5//n/+Yf5h/jf+N/4V/hX+Df4N/iX+Jf5T/lP+gP6A/pX+lf6E/oT+U/5T/hn+Gf7v/e/95P3k/fn9+f0c/hz+OP44/kP+Q/49/j3+Mf4x/i3+Lf48/jz+Xv5e/pD+kP7I/sj++f75/hf/F/8a/xr///7//tH+0f6g/qD+fv5+/nT+dP6D/oP+of6h/sT+xP7m/ub+BP8E/yL/Iv8//z//Vf9V/1//X/9Y/1j/Rv9G/zT/NP8v/y//PP88/1z/XP+E/4T/qP+o/7z/vP+8/7z/qP+o/4f/h/9j/2P/S/9L/03/Tf9y/3L/tP+0/wEAAQA9AD0ATwBPACwALADi/+L/kf+R/1z/XP9X/1f/ff99/7f/t//m/+b/+//7//j/+P/w//D/9//3/xUAFQA/AD8AYwBjAHAAcABlAGUASwBLADQANAAoACgAKQApAC8ALwAyADIAMQAxAC4ALgAuAC4AMQAxADEAMQAoACgAFgAWAAEAAQD0//T/+v/6/xUAFQA/AD8AbABsAI0AjQCYAJgAiwCLAGkAaQA9AD0AFQAVAPv/+//1//X//////w0ADQATABMABgAGAOf/5//B/8H/o/+j/5r/mv+o/6j/wf/B/9T/1P/O/87/p/+n/2b/Zv8g/yD/7/7v/un+6f4P/w//T/9P/4r/iv+e/57/ev96/yX/Jf++/r7+aP5o/kX+Rf5d/l3+o/6j/vf+9/40/zT/P/8//w3/Df+t/q3+Pf49/ub95v3G/cb94/3j/S3+Lf58/nz+pf6l/pX+lf5Y/lj+Ef4R/ur96v34/fj9L/4v/m3+bf6O/o7+gf6B/lb+Vv4o/ij+Ef4R/hr+Gv4y/jL+Pv4+/jL+Mv4P/g/+6v3q/d393f34/fj9N/43/on+if7O/s7+7P7s/tX+1f6V/pX+Sv5K/h7+Hv4s/iz+d/53/t3+3f4v/y//Q/9D/xX/Ff/G/sb+iv6K/on+if7G/sb+Iv8i/3L/cv+Z/5n/mP+Y/4L/gv9s/2z/WP9Y/z3/Pf8Y/xj/+f75/vz+/P44/zj/p/+n/yEAIQBwAHAAcwBzACsAKwDA/8D/ZP9k/zv/O/9Q/1D/lf+V//P/8/9SAFIAmQCZALEAsQCNAI0ANAA0AMn/yf+A/4D/e/97/7j/uP8NAA0ARQBFAEMAQwAaABoA/v/+/xoAGgBtAG0AxgDGAOYA5gCuAK4ANwA3AMX/xf+b/5v/z//P/0IAQgCzALMA7QDtANsA2wCQAJAALQAtANL/0v+Y/5j/jf+N/7v/u/8ZABkAiQCJAN0A3QDqAOoApQClACwALAC0/7T/b/9v/27/bv+j/6P/8//z/0kASQCfAJ8A6ADoAAgBCAHcANwAWABYAKH/of8M/wz/7/7v/m3/bf9QAFAAJAEkAXgBeAEiASIBWABYAIj/iP8X/xf/Lf8t/67/rv9WAFYA7ADsAEoBSgFiAWIBLwEvAa4ArgD+//7/Wv9a/xT/FP9j/2P/NgA2ADIBMgHdAd0B5gHmAVcBVwGIAIgA7f/t/8T/xP//////VwBXAIUAhQB6AHoAYgBiAHUAdQDQANAAVQFVAcABwAHRAdEBdQF1AckAyQAOAA4Ahv+G/1v/W/+c/5z/NQA1AAABAAHCAcIBOwI7AjoCOgK0AbQB0QDRAOH/4f9C/0L/Mv8y/7r/uv+iAKIAkQGRAS8CLwJIAkgC4AHgASgBKAFsAGwA7v/u/9P/0/8ZABkAngCeAC8BLwGeAZ4BzAHMAawBrAFPAU8B1wDXAHcAdwBhAGEAswCzAGABYAEpAikCtQK1ArUCtQIUAhQCDwEPARkAGQCu/67/CwALAA4BDgFFAkUCJgMmA1ADUAPDAsMC1gHWAQIBAgGsAKwA7ADsAIsBiwEtAi0CggKCAnQCdAIuAi4C+QH5AQYCBgJRAlECngKeAqECoQJAAkACowGjASgBKAEmASYBtAG0AZQClAJZA1kDqAOoA2gDaAPOAs4CMgIyAtIB0gG9Ab0B2gHaAQYCBgI2AjYCcQJxArkCuQL9Av0CGQMZA/IC8gKOAo4CFQIVArMBswGBAYEBfAF8AZMBkwHAAcABCAIIAmkCaQLIAsgC7ALsAqMCowLtAe0BEAEQAXsAewB+AH4AFgEWAeUB5QFnAmcCRgJGAooBigGWAJYA6f/p/9L/0v9KAEoAAwEDAZYBlgG4AbgBVgFWAZkAmQDI/8j/Mf8x/wX/Bf9N/03/3P/c/2gAaACpAKkAfwB/AAAAAABm/2b/6/7r/qf+p/6L/ov+ev56/mz+bP5y/nL+ov6i/vT+9P47/zv/OP84/9T+1P4z/jP+p/2n/Xj9eP2s/az9/v3+/RX+Ff7G/cb9O/07/d383fwF/QX9tv22/Zb+lv4c/xz/6P7o/gL+Av7R/NH84vvi+5/7n/sc/Bz8HP0c/Tz+PP4U/xT/Xf9d//r++v4F/gX+yfzJ/MD7wPth+2H78fvx+1H9Uf3z/vP+FwAXACoAKgAb/xv/bP1s/fv7+/uC+4L7OPw4/LP9s/0o/yj/3v/e/5X/lf+b/pv+kf2R/Qj9CP0v/S/9yf3J/Wf+Z/64/rj+tP60/pX+lf6f/p/+6/7r/lf/V/+m/6b/sP+w/3n/ef8p/yn/6f7p/sj+yP65/rn+sv6y/r3+vf73/vf+bv9u/wYABgB5AHkAfwB/AAQABABD/0P/p/6n/pX+lf4p/yn/JQAlABwBHAGpAakBogGiARgBGAFGAEYAav9q/7j+uP5l/mX+oP6g/ob/hv/wAPAAaAJoAkEDQQPvAu8CZQFlATb/Nv9r/Wv99vz2/DH+Mf6WAJYAAgMCAz4EPgSuA64DoAGgARn/Gf9S/VL9EP0Q/VH+Uf5bAFsAMgIyAhkDGQPjAuMC7wHvAdQA1AAMAAwAvP+8/73/vf/U/9T/4f/h/+//7/8hACEAiACIAA4BDgF9AX0BowGjAW0BbQHtAO0ASgBKAKb/pv8f/x//1P7U/u/+7/6Q/5D/qACoAOEB4QGoAqgCdAJ0Ah8BHwEh/yH/af1p/e387fwP/g/+WgBaAK0CrQLNA80DFwMXA9YA1gAk/iT+T/xP/DL8MvzK/cr9SQBJAIgCiAKOA44D9AL0Av8A/wB1/nX+T/xP/F/7X/sI/Aj8FP4U/rsAuwDsAuwCuQO5A8kCyQKMAIwAC/4L/mv8a/xU/FT8ov2i/YX/hf/+AP4AZgFmAb8AvwCP/4//hP6E/g7+Dv4w/jD+ov6i/g7/Dv9I/0j/Uv9S/0P/Q/8v/y//H/8f/x7/Hv8z/zP/Xf9d/4D/gP9r/2v//P78/lD+UP7G/cb9z/3P/Zz+nP7b/9v/0QDRAMcAxwCN/43/sv2y/UT8RPwv/C/8ov2i/en/6f/OAc4BTQJNAiYBJgH7/vv+6Pzo/Of75/tc/Fz8/P38/Q8ADwDBAcEBcAJwAtEB0QESABIA0f3R/QT8BPyZ+5n7+vz6/Lz/vP+gAqACKwQrBG0DbQOjAKMAI/0j/bL6svqI+oj6qPyo/O7/7v+6AroC0wPTA/AC8AK9AL0AVf5V/rn8ufxj/GP8P/0//dX+1f6CAIIAsAGwAQICAgJ2AXYBaQBpAGr/av/v/u/+Dv8O/3b/dv+2/7b/lP+U/07/Tv9e/17/FwAXAEABQAEiAiIC/QH9AZwAnACd/p3+Hv0e/RH9Ef2c/pz+/QD9AAADAAOsA6wDxgLGAswAzACq/qr+O/07/Qj9CP0r/iv+VQBVANEC0QKjBKME2QTZBAgDCAPA/8D/dfx1/OH64fr++/77YP9g/0wDTAOtBa0FTAVMBYICggL6/vr+nvye/Hr8evxD/kP+uwC7AJYClgI0AzQDwgLCAtAB0AHWANYABAAEAGX/Zf8e/x7/bf9t/2cAZwC4AbgBtgK2AssCywLlAeUBlgCWAK3/rf+j/6P/RwBHAPAA8AAOAQ4BpACkAD4APgB1AHUAYAFgAWkCaQLAAsACAAIAAogAiABJ/0n/GP8Y/x0AHQC3AbcB9gL2AjMDMwN0AnQCQwFDAT8APwDG/8b/2v/a/1gAWAAdAR0BCQIJAt4C3gI5AzkDvgK+AmQBZAGt/63/a/5r/lT+VP6L/4v/iAGIAWEDYQNLBEsEAQQBBL0CvQIHAQcBZP9k/zj+OP7L/cv9Sf5J/rX/tf+4AbgBoQOhA5kEmQQQBBAEGgIaAnb/dv8+/T79Wvxa/B39Hf0l/yX/nwGfAaEDoQOABIAE/wP/A1ICUgISABIAD/4P/gf9B/1a/Vr92f7Z/tsA2wCHAocCRANEA/QC9ALsAewBqgCqAJL/kv/Q/tD+ev56/qr+qv53/3f/yQDJADQCNAIMAwwDwQLBAkMBQwEw/zD/hP2E/Rv9G/0y/jL+SwBLAHcCdwLPA88D1wPXA5QClAKAAIAAUP5Q/sz8zPyS/JL81/3X/TsAOwDMAswCZgRmBEUERQR8AnwC+P/4//f99/1Z/Vn9Iv4i/pv/m//hAOEAeAF4AXoBegFXAVcBWgFaAXEBcQFNAU0BygDKABUAFQCZ/5n/m/+b/wEAAQBvAG8ApACkALcAtwD8APwAogGiAV8CXwKKAooCpAGkAdH/0f/0/fT9JP0k/er96v3f/9//5wHnAfMC8wKrAqsCjwGPAXIAcgDg/+D/yv/K/8//z/+q/6r/gP+A/6P/o/8qACoAuwC7AMgAyAAbABsAJf8l/7v+u/5r/2v/8QDxAD8CPwI+Aj4CnwCfACn+Kf5F/EX8B/wH/IL9gv3N/83/tQG1AXsCewIiAiICHwEfAdr/2v94/nj+Cf0J/eT75Puq+6r72fzZ/EL/Qv/cAdwBRwNHA6cCpwJJAEkAeP14/bb7tvu9+737Jf0l/cL+wv6H/4f/MP8w/0z+TP62/bb97f3t/cz+zP65/7n/IAAgANH/0f///v/+Cf4J/kH9Qf3O/M78uPy4/Pv8+/yI/Yj9Pf49/vT+9P6F/4X/4v/i/wcABwDf/9//Pv8+/wn+Cf52/Hb8Ifsh+9363foo/Cj8wv7C/pwBnAFWA1YDAwMDA7UAtQB//X/92PrY+uf55/n0+vT6cv1y/V4AXgClAqUCgAOAA5sCmwI5ADkANv02/eL64vpu+m76RfxF/J//n/+7ArsCzQPNAx8CHwKi/qL+b/tv+4j6iPqG/Ib8PwA/AIADgAN2BHYEwwLDApf/l//G/Mb8n/uf+0D8QPzY/dj9a/9r/4EAgQA3ATcB2wHbAWoCagJ+An4CqwGrAfT/9P/1/fX9kfyR/Hb8dvy2/bb90v/S/wkCCQKuA64DVQRVBNAD0AMzAjMC2v/a/3j9eP38+/z7L/wv/Er+Sv6lAaUB8ATwBK8GrwYDBgMGDwMPA/3+/f6I+4j7NPo0+qv7q/tk/2T/1APUAxgHGAfIB8gHogWiBaEBoQGN/Y39FPsU+wv7C/ss/Sz9YQBhAGwDbANkBWQF5wXnBfAE8AS4ArgCuv+6/8/8z/wa+xr7k/uT+2j+aP6WApYCQQZBBqAHoAf+Bf4FKgIqAvX99f00+zT7xvrG+nD8cPw8/zz/NQI1AqsEqwQfBh8GEAYQBg8EDwRUAFQAGPwY/GD5YPng+eD5yv3K/VEDUQN2B3YHywfLB/cD9wMS/hL+YPlg+Wj4aPh4+3j7qwCrACkFKQXSBtIGPgU+BZgBmAG3/bf9I/sj+5L6kvrw+/D7n/6f/rkBuQEqBCoE/QT9BLQDtAOsAKwAE/0T/Wn6afrQ+dD5b/tv+4f+h/7MAcwBEwQTBKYEpgRlA2UDpgCmADL9Mv0v+i/63vje+Bj6GPqv/a/9PwI/ArcFtwVdBl0G0APQAzj/OP+4+rj6N/g3+JL4kvhY+1j7VP9U/ykDKQOnBacF9gX2BaYDpgMi/yL/6/nr+Wf2Z/a79rv2b/tv+50CnQKaCJoI9wn3CaMFowXM/cz9sPaw9h70HvQ/9z/3Ef4R/uUE5QSPCI8IDggOCG8EbwS1/7X/hfuF+8T4xPjr9+v3Z/ln+WX9Zf0JAwkDLAgsCBMKEwolByUHRgBGAMD4wPiK9Ir00fXR9bj7uPvmAuYCxQfFB40IjQj0BfQFHgIeAtv+2/7E/MT8g/uD++z67PqE+4T7Af4B/icCJwJCBkIG7gfuB8EFwQWVAJUARPtE+9z43Pif+p/6Mf8x/7UDtQO5BbkFxwTHBDcCNwLt/+3/4P7g/sH+wf7K/sr+wv7C/j7/Pv/YANgANQM1A9oE2gQ5BDkEEgESAQr9Cv3F+sX6BPwE/FIAUgAjBSMFjQeNByUGJQbnAecBW/1b/eH64fpX+1f79f31/TwBPAHtA+0DfgV+Bb0FvQWDBIMEvQG9Aff99/2i+qL6kfmR+dv72/vVANUALAYsBh8JHwklCCUI3APcA4X+hf6u+q76uPm4+X37ffvB/sH+MQIxAuEE4QQ+Bj4G2wXbBX0DfQOd/53/q/ur+6r5qvn7+vv6TP9M/3MEcwSQB5AH4QbhBtkC2QLq/er94vri+jX7Nfs2/jb+0AHQAf8D/wP5A/kDTwJPAigAKAB1/nX+kf2R/Yr9iv1o/mj+KgAqAGECYQILBAsE/QP9A74BvgEt/i3+Ovs6+8j6yPpY/Vj9mQGZAScFJwUDBgMGzAPMA9H/0f8w/DD8gPqA+jH7MfuX/Zf9oACgAFsDWwMMBQwFIQUhBTUDNQOI/4j/SftJ+2v4a/ij+KP4TvxO/OkB6QGxBrEGMwgzCLAFsAWUAJQAhvuG+9743vhx+XH5bfxt/C0ALQAyAzIDuQS5BJoEmgQAAwADPQA9AP78/vxw+nD64Png+Qf8B/w8ADwAjQSNBKUGpQY4BTgF3wDfAN/73/vj+OP4cvly+Tb9Nv00AjQCDQYNBhgHGAcVBRUFBQEFAY/8j/xr+Wv50fjR+Cf7J/u1/7X/wATABAsICwjhB+EHBQQFBBL+Ev7X+Nf46vbq9kn5SfnD/sP+rASsBEUIRQggCCAIqwSrBLD/sP9g+2D7TflN+f75/vnw/PD88ADwAIUEhQRbBlsGugW6Bc8CzwLK/sr+ZPtk+x/6H/qB+4H71P7U/osCiwIOBQ4FgwWDBQQEBARhAWEBnP6c/oH8gfyN+4379fv1+679rv1QAFAACQMJA9ME0wTZBNkE/AL8Auf/5//j/OP8Kvsq+3H7cfuU/ZT9vgC+ALoDugNaBVoF2wTbBCwCLAIu/i7+gfqB+vT49PiQ+pD65/7n/ggECAReB14HGQcZBzMDMwOe/Z79J/kn+f73/veL+ov6Yf9h/yYEJgTGBsYGWgZaBj8DPwPK/sr+nfqd+kL4Qvi0+LT4BfwF/CgBKAENBg0GbQhtCN4G3gbKAcoBZ/tn+8D2wPYX9hf2s/mz+c7/zv+CBYIFXwhfCG8Hbwd1A3UDPP48/rb5tvla91r31vfW9xn7GfszADMAfQV9BeII4gi2CLYIoQShBBT+FP7n9+f3+vT69MX2xfZ0/HT8WANYA0UIRQgsCSwJAgYCBn4AfgAp+yn7G/gb+E/4T/hn+2f7EwATAIkEiQQUBxQHpAakBjYDNgMd/h3+kfmR+dH30ffo+ej5A/8D/64ErgQKCAoIXgdeBwwDDANe/V79Ofk5+Y34jfhb+1v77//v/wAEAATkBeQFQQVBBdEC0QLJ/8n/Kf0p/Zz7nPuC+4L7Df0N/RAAEACsA6wDZAZkBqQGpAbIA8gDvv6+/tn52fmx97H3ofmh+ef+5/7sBOwEsgiyCHAIcAiABIAE//7//ob6hvrc+Nz4QfpB+r/9v/3QAdABFwUXBaAGoAb4BfgFLwMvAwj/CP8C+wL74/jj+O357fn1/fX9QgNCA0oHSgcZCBkIbQVtBc8AzwCc/Jz8n/qf+iz7LPtS/VL9vP+8/6IBogHxAvEC2wPbAywELARXA1cDAQEBAbz9vP0a+xr7zvrO+nj9eP3sAewBuwW7BaQGpAYPBA8EdP90/277bvsf+h/68Pvw+4r/iv/RAtECOwQ7BIADgANxAXEBMv8y/5v9m/0B/QH9bv1u/c/+z/7XANcA1gLWAsUDxQPVAtUCDAAMAJn8mfxT+lP6o/qj+pj9mP2zAbMBxQTFBDcFNwX2AvYCZ/9n/3T8dPxo+2j7U/xT/Ez+TP4rACsAPQE9AXUBdQEdAR0BcwBzAIn/if92/nb+hv2G/UH9Qf0D/gP+pv+m/2YBZgFAAkACjgGOAXr/ev8E/QT9cvty+7T7tPvO/c794QDhAIsDiwOMBIwEVgNWA00ATQCn/Kf88vny+W35bflu+277L/8v/xgDGANqBWoFHgUeBVYCVgJd/l3+9/r3+pH5kfmj+qP6kf2R/RMBEwHJA8kDxATEBLwDvAMgASAB4v3i/Tr7Ovs9+j36cftx+3n+ef4UAhQCoQShBOgE6ATJAskCWv9a/1X8Vfwq+yr7Q/xD/O3+7f7LAcsBqQOpA+kD6QOpAqkCcwBzAAD+AP4c/Bz8jPuM+9j82Pzb/9v/jAOMAzYGNgZVBlUGhAOEA/X+9f7x+vH6kvmS+Y77jvvI/8j/BwQHBEYGRga+Bb4FHAMcA8z/zP8r/Sv97fvt+z38Pfzu/e79qwCrAMIDwgMMBgwGRgZGBscDxwNI/0j/zPrM+rj4uPhf+l/6H/8f/50EnQQQCBAI2gfaB0wETARZ/1n/Wvta++P54/k0+zT7fP58/m4CbgKwBbAFLQctBy0GLQbEAsQCA/4D/uv56/mU+JT4BvsG+2kAaQAnBicGQwlDCfsH+wfsAuwCsvyy/Hr4evhF+EX46/vr+3IBcgErBisGIQghCMIGwgbuAu4CU/5T/r/6v/qA+YD5CPsI+8P+w/4xAzEDcgZyBgcHBweeBJ4EUABQACD8IPwB+gH60PrQ+v79/v3nAecBwwTDBHYFdgXwA/AD+wD7ALz9vP1W+1b7nPqc+vT79PsX/xf/CAMIAzQGNAYLBwsHzgTOBB4AHgD2+vb6vve+9yT4JPgc/Bz84QHhAdQG1Aa/CL8I5gbmBjgCOALK/Mr8y/jL+Kv3q/ej+aP5zv3O/ZoCmgJKBkoGkQeRB+YF5gXZAdkB7Pzs/B35Hfka+Br4c/pz+kP/Q/9rBGsEkQeRBzEHMQd1A3UDGP4Y/pj5mPn+9/735fnl+Vf+V/5KA0oDqgaqBjAHMAfRBNEElACUABD8EPzq+Or4TvhO+KT6pPo9/z3/ewR7BDEIMQiSCJIIGAUYBf/+//77+Pv47fXt9Xv3e/cL/Qv9BAQEBBMJEwngCeAJOAY4BgIAAgA8+jz6a/dr95D4kPjW/Nb8OQI5AnYGdgbxB/EHOwY7BhICEgIi/SL9Yflh+WH4Yfih+qH6Qv9C/1cEVwSrB6sH0AfQB7oEugTX/9f/TftN+wb5Bvnb+dv5UP1Q/egB6AHBBcEFUwdTB/cF9wUrAisCbf1t/bb5tvmr+Kv42PrY+l3/Xf83BDcEPQc9BxsHGwcHBAcEh/+H/6D7oPvh+eH5u/q7+pP9k/0kASQBHgQeBHwFfAXDBMMEHAIcAmH+Yf4F+wX7j/mP+er66vq4/rj+TwNPA3cGdwaWBpYGmwObAwP/A/8Q+xD7evl6+bH6sfrJ/cn9NQE1AZgDmANBBEEEMQMxA9cA1wD0/fT9gPuA+4z6jPrD+8P78P7w/sUCxQJcBVwFOQU5BUICQgLv/e/9fvp++qX5pfmb+5v7Gf8Z/0UCRQLJA8kDcANwA98B3wHy//L/NP40/t783vwo/Cj8bvxu/Pn9+f2BAIEAAgMCAy4ELgRDA0MDpwCnAMf9x/00/DT8ofyh/H7+fv5xAHEAWQFZAQEBAQEaABoAkP+Q/8T/xP9iAGIAxgDGAJoAmgAVABUAuv+6/8n/yf8KAAoABQAFAIb/hv/k/uT+xv7G/pT/lP8FAQUBQgJCAnkCeQJ6AXoB2P/Y/4D+gP4U/hT+jf6N/m3/bf8yADIAuwC7ADUBNQHEAcQBOAI4AhwCHAIlASUBkP+Q/xv+G/6Y/Zj9Wf5Z/vP/8/+BAYEBOwI7AvAB8AEOAQ4BQQBBAPX/9f8aABoAUQBRAD4APgDY/9j/Y/9j/zv/O/+T/5P/WABYAEMBQwH6AfoBNwI3AtMB0wHRANEAav9q/wz+DP5E/UT9g/2D/eb+5v4IAQgBIQMhA1AEUAQGBAYESwJLAsT/xP9z/XP9QfxB/KD8oPxQ/lD+ggCCADwCPALLAssCGwIbArAAsABW/1b/rf6t/uT+5P6p/6n/eAB4AOUA5QDLAMsAQgBCAHj/eP+k/qT+BP4E/vD98P2t/q3+PAA8ABUCFQJEA0QD5wLnAscAxwCy/bL9K/sr+5z6nPp0/HT81v/W/xQDFAOcBJwE2wPbA2MBYwF6/nr+UPxQ/H37ffv1+/X7Wf1Z/T7/Pv85ATkByQLJAlwDXAOWApYCqQCpAGf+Z/7j/OP8yfzJ/PX99f2P/4//pwCnANsA2wB/AH8ANgA2AFAAUACJAIkAUgBSAG//b/9O/k7+yf3J/XH+cf4CAAIAegF6AdoB2gHtAO0Aff99/7j+uP45/zn/igCKAHcBdwEMAQwBYP9g/5v9m/0b/Rv9av5q/swAzAC7ArsC+wL7Am4BbgEW/xb/Vv1W/Qv9C/0c/hz+vv++/xgBGAG9Ab0BtgG2AToBOgFyAHIAeP94/4X+hf77/fv9KP4o/gH/Af8KAAoAoQChAHgAeADZ/9n/dP90/8j/yP+1ALUAhAGEAXIBcgFZAFkA2f7Z/vb99v1P/k/+qP+o/wkBCQFzAXMBkACQAPH+8f6r/av9lf2V/c3+zf6qAKoAOQI5Ar8CvwIaAhoCtQC1AEL/Qv9h/mH+Z/5n/jr/Ov9lAGUAQgFCAVEBUQGHAIcAYP9g/6H+of7Y/tj+9P/0/zsBOwHIAcgBLQEtAcn/yf+S/pL+Zv5m/mj/aP/rAOsA6wHrAcwBzAHCAMIAmf+Z/xb/Fv9l/2X/EQARAHMAcwBBAEEAw//D/4P/g//X/9f/kACQACsBKwE1ATUBqACoAN//3/9R/1H/Nf81/4P/g/8MAAwAsgCyAFkBWQHDAcMBjQGNAWcAZwCG/ob+wvzC/Ef8R/y//b/9vQC9ALoDugP9BP0EtAO0A5wAnACR/ZH9Xfxd/If9h/0JAAkAFgIWAmoCagIZARkBX/9f/5j+mP40/zT/dgB2AB0BHQF2AHYA5P7k/pj9mP2h/aH9Hf8d/yYBJgGAAoACeAJ4AlQBVAEEAAQAbf9t/8//z/+xALEAVwFXAUIBQgF4AHgAXf9d/2T+ZP7h/eH9AP4A/t/+3/5zAHMAaQJpAggECARzBHMEKgMqA4IAggCs/az9Gvwa/Kv8q/wG/wb/ywHLAWQDZAMJAwkDMwEzATr/Ov9m/mb+E/8T/40AjQCqAaoBrQGtAcMAwwDG/8b/hP+E/xwAHADyAPIAOgE6AaYApgCp/6n/Gv8a/4T/hP+pAKkAqgGqAa8BrwGHAIcA0v7S/o/9j/11/XX9hP6E/i4ALgC9Ab0ByALIAi8DLwPqAuoC2AHYAer/6v+G/Yb9qvuq+437jfvI/cj9qgGqAUwFTAWTBpMGgwSDBAMAAwBz+3P7RPlE+YH6gfoz/jP+HQIdAjAEMAS+A74DqQGpAYX/hf9+/n7+r/6v/nP/c/8NAA0ARQBFAFMAUwByAHIAgACAABoAGgAe/x7/AP4A/qT9pP2m/qb+wADAAMYCxgJhA2EDAAIAAln/Wf/z/PP8K/wr/Ev9S/1s/2z/LwEvAbIBsgESARIBIgAiAKb/pv/B/8H/+v/6/87/zv81/zX/s/6z/tj+2P6y/7L/rACsAAUBBQF1AHUAgP+A/xP/E/+7/7v/IgEiATECMQLdAd0B/f/9/4D9gP3g++D7H/wf/CD+IP7EAMQAqwKrAhEDEQMlAiUCtQC1AIL/gv/W/tb+i/6L/nH+cf6X/pf+PP88/3UAdQDfAd8BvQK9AmwCbALeAN4Av/6//hH9Ef2Y/Jj8bv1u/Qz/DP+rAKsAtwG3AQECAQKlAaUB2gDaAM//z//C/sL+Cv4K/gb+Bv7Z/tn+OAA4AHQBdAHUAdQBDAEMAX3/ff8G/gb+g/2D/Uv+S/79//3/vgG+AbcCtwJ/An8CPQE9AXj/eP/Z/dn95/zn/Pj8+PwQ/hD+4//j/9AB0AEGAwYD3wLfAj4BPgHE/sT+lvyW/NT71Pvz/PP8eP94/zECMQLWA9YDtAO0A/MB8wF8/3z/av1q/ZP8k/w0/TT9+f75/iwBLAHsAuwCfgN+A38CfwIsACwAYP1g/Vb7Vvsj+yP7HP0c/ZAAkAAABAAE1gXWBSYFJgUzAjMCQP5A/vj6+Pqw+bD53Prc+gr+Cv4IAggCbQVtBfMG8wb2BfYFswKzAlX+Vf6i+qL6P/k/+eL64vrU/tT+OAM4A/gF+AXeBd4FMwMzA3X/df+D/IP8ivuK+6n8qfwV/xX/wgHCAdMD0wO3BLcEGwQbBOIB4gGF/oX+OPs4+7T5tPlI+0j73//f/5IFkgV2CXYJOgk6CacEpwT4/fj9mPiY+Cz3LPcS+hL6Xv9e/zwEPASbBpsGKgYqBusD6wM4ATgB1f7V/u787vyk+6T7e/t7+xb9Fv1sAGwARQRFBJ4GngbrBesFSwJLAqj9qP2f+p/6zfrN+sr9yv2LAYsB1gPWA7QDtAPbAdsB2f/Z/8v+y/66/rr+6P7o/sn+yf6U/pT+Av8C/2sAawAxAjECBAMEA+sB6wEx/zH/Zvxm/Ff7V/vE/MT80//T/7ACsALMA8wD4wLjAugA6AAj/yP/Nv42/uT95P2f/Z/9Tf1N/YL9gv3i/uL+UgFSAawDrANiBGIEpgKmAiL/Iv+2+7b7RPpE+oP7g/uQ/pD+pQGlAUMDQwMEAwQDkgGSAeL/4v+S/pL+sv2y/Sz9LP0j/SP97v3u/an/qf/JAckBOAM4AwADAAMRAREBb/5v/qv8q/zR/NH8uP64/hwBHAFwAnAC5AHkAdX/1f+F/YX9RPxE/LX8tfyM/oz+4gDiALoCugJrA2sDyQLJAhQBFAHd/t3+5/zn/Ov76/ta/Fr8Fv4W/nQAdAB5AnkCXgNeA+QC5AJkAWQBiv+K//H98f3w/PD8mvya/PT89PwB/gH+qP+o/48BjwELAwsDaQNpA0wCTAICAAIAbv1u/aX7pftm+2b7yPzI/Dr/Ov/FAcUBeAN4A7ADsANRAlEC0//T/yT9JP1i+2L7a/tr+279bv2oAKgAswOzAxoFGgUlBCUEOwE7Abb9tv01+zX7y/rL+or8ivyN/43/ggKCAkEEQQQ4BDgEhwKHAtj/2P8r/Sv9gvuC+537nfua/Zr91ADUAAIEAgS5BbkFHgUeBVoCWgKm/qb+r/uv+8b6xvo//D/8U/9T/5kCmQK0BLQE6wTrBFUDVQOxALEABP4E/jr8Ovzp++n7Jf0l/YX/hf8xAjECNQQ1BNYE1gTgA+ADwwHDAVH/Uf9p/Wn9k/yT/Ov86/wy/jL+AQABAOMB4wFfA18DAwQDBH4DfgPhAeEBt/+3/+X95f09/T39CP4I/tP/0/+mAaYBkQKRAjQCNALqAOoAgP+A/7f+t/7c/tz+vP+8/+EA4QDQAdABPgI+AgECAQIUARQBn/+f/xn+Gf47/Tv9s/2z/bL/sv+TApMCEQURBdgF2AVVBFUEGAEYAY/9j/1L+0v7Jvsm+/L88vy5/7n/XQJdAiMEIwTIBMgEXQRdBPYC9gK+AL4AHf4d/uL74vsP+w/7SfxJ/GD/YP8nAycD+wX7BY8GjwahBKEEFQEVAXr9ev1G+0b7Nfs1+xz9HP0eAB4AGAMYAwIFAgU0BTQFkQORA5gAmABe/V79Mfsx+xv7G/tL/Uv95gDmAFAEUATrBesF7ATsBL8BvwHe/d79DvsO+476jvqC/IL89P/0/1oDWgNFBUUF/QT9BLoCugKD/4P/svyy/Gj7aPsa/Br8Zv5m/kcBRwGEA4QDPAQ8BDcDNwP7APsAfP58/rb8tvw+/D78Gf0Z/cn+yf6WAJYA2gHaAUQCRALiAeIBEwETAUEAQQCt/63/UP9Q//f+9/6B/oH+Cf4J/vH98f2X/pf+BgAGAMUBxQH9Av0C8gLyAnUBdQEZ/xn/6/zr/Ob75vty/HL8Nf41/l4AXgAOAg4CwgLCAmcCZwI8ATwBov+i/wT+BP7V/NX8gfyB/Eb9Rv33/vf+8gDyAFICUgJsAmwCNgE2AV7/Xv/p/en9nP2c/YT+hP7z//P/9QD1AOcA5wDf/9//jf6N/sz9zP0R/hH+Mf8x/4QAhABSAVIBOQE5AVYAVgAo/yj/Pf49/vD98P1R/lH+Mv8y/0kASQBFAUUB4wHjAeMB4wEqASoBzv/O/zX+Nf73/Pf8q/yr/I79jv1V/1X/QAFAAXUCdQJyAnICVwFXAcj/yP+J/on+Ef4R/lr+Wv4D/wP/rf+t/zMAMwCpAKkAGAEYAVYBVgEUARQBLgAuAPD+8P4G/gb+Ff4V/jb/Nv/QANAA6QHpAdEB0QGhAKEALf8t/27+bv7U/tT++f/5//sA+wAsASwBjACMALr/uv9l/2X/vP+8/1AAUACCAIIAEAAQAFn/Wf8K/wr/jf+N/6AAoACDAYMBiQGJAaAAoABm/2b/r/6v/u/+7/7o/+j/7wDvAGcBZwElASUBbABsAJj/mP/i/uL+Wf5Z/hn+Gf5f/l/+Zv9m/wABAAF6AnoC4gLiArABsAFS/1L/E/0T/V/8X/zG/cb9jgCOABYDFgPdA90DfAJ8AtD/0P9j/WP9bPxs/Cn9Kf3o/uj+pgCmALIBsgHnAecBeAF4AaMAowCU/5T/hv6G/uX95f0o/ij+cf9x/0cBRwGwArACyQLJAlwBXAEf/x//Rv1G/cv8y/zS/dL9qf+p/08BTwEWAhYC9QH1AVkBWQG6ALoARgBGAOz/7P+S/5L/Rf9F/zT/NP97/3v//P/8/3sAewDDAMMA0gDSAMcAxwCvAK8AbgBuANv/2/8I/wj/Yf5h/nn+ef6Z/5n/agFqAQYDBgN/A38DegJ6AncAdwCK/or+pf2l/Rb+Fv5i/2L/swCzAGcBZwFcAVwB1gDWADAAMACd/53/Lv8u//7+/v49/z3/CQAJAC4BLgEXAhcCGgIaAvYA9gAl/yX/q/2r/YD9gP3j/uP+IwEjAf4C/gJlA2UDFQIVArn/uf98/Xz9Y/xj/NL80vx+/n7+sgCyAKcCpwLAA8ADqQOpA1kCWQIrACsA1f3V/UX8Rfw8/Dz84/3j/ZwAnAArAysDVARUBHADcAPhAOEA2f3Z/dP70/vL+8v7yP3I/dcA1wCLA4sDqwSrBMIDwgNTAVMBhv6G/pX8lfw5/Dn8X/1f/VL/Uv8nAScBMwIzAkgCSAKdAZ0BjACMAFn/Wf83/jf+Xf1d/SP9I/3c/dz9i/+L/60BrQFTA1MDlwOXAyoCKgKa/5r/FP0U/b77vvse/B781v3W/QEAAQCxAbEBawJrAisCKwI1ATUB0P/Q/0f+R/4D/QP9i/yL/E79Tv0z/zP/bwFvAdEC0QJ8AnwCiACIABv+G/6t/K38Ff0V/er+6v7TANMAfAF8AZ0AnQAZ/xn/T/5P/vP+8/55AHkAeQF5AdgA2ADA/sD+qfyp/FH8Ufxj/mP+2gHaAZcElwTJBMkEKgIqAij+KP78+vz6O/o7+gf8B/wq/yr/EgISArMDswPaA9oD3ALcAhEBEQHE/sT+Zfxl/Nj62Poq+yr73f3d/TYCNgI/Bj8Gvwe/B5AFkAWNAI0AN/s3+1b4VvhZ+Vn5jf2N/aECoQIPBg8GewZ7BjUENQS6ALoAr/2v/RP8E/wY/Bj8b/1v/an/qf9CAkICigSKBKIFogXUBNQEGwIbAmL+Yv5L+0v7Wvpa+iL8Ivzj/+P/7gPuA34GfgaTBpMGUgRSBMkAyQBj/WP9VPtU+0D7QPsb/Rv9OAA4AHkDeQOjBaMF0QXRBd4D3gOSAJIAUP1Q/Xr7evvC+8L73f3d/bgAuAAQAxADDQQNBI0DjQMIAggCNwA3AL7+vv7+/f79D/4P/tT+1P4CAAIANQE1AQ4CDgJSAlICCQIJAmkBaQGtAK0A8f/x/zX/Nf+B/oH+Df4N/jL+Mv4s/yz/zwDPAHgCeAJUA1QD4gLiAkQBRAFB/0H/0P3Q/ZL9kv2B/oH+CgAKAG8BbwEjAiMC+AH4AQYBBgGe/57/Mv4y/lf9V/2S/ZL9EP8Q/1kBWQFYA1gD1wPXAzwCPAIY/xj//Pv8+6n6qfr++/77Yv9i/w4DDgMQBRAFaARoBH8BfwHk/eT9TftN+836zfpl/GX8O/87/yACIAL/A/8DLgQuBI0CjQKr/6v/qPyo/O/67/qA+4D7WP5Y/jICMgIMBQwFQAVABYMCgwI5/jn+tPq0+u357flP/E/8hQCFAEQERASaBZoF9gP2A1IAUgCc/Jz8oPqg+kX7Rfsq/ir++gH6AfoE+gTHBccF4gPiA/f/9/+5+7n7J/kn+ab5pvkn/Sf9EwITAvkF+QXbBtsGRQRFBI//j/8s+yz7SPlI+bX6tfqE/oT+rgKuAikFKQXxBPEEXQJdAtH+0f4F/AX8N/s3+7j8uPy//7//zwLPAlUEVQR5A3kDlACUAB39Hf34+vj6Yvti+0D+QP4PAg8CyQTJBAMFAwW8ArwCS/9L/378fvyU+5T7p/yn/Nf+1/71APUAMgIyAl0CXQK9Ab0BtgC2AJ7/nv+8/rz+VP5U/pL+kv5i/2L/WwBbAPEA8QDVANUAOQA5ALX/tf/J/8n/cwBzABEBEQHgAOAAmv+a/9L90v2s/Kz8Fv0W/R3/Hf/BAcEBkQORA30DfQOKAYoBvv6+/o78jvwI/Aj8WP1Y/cj/yP8oAigCYgNiA+sC6wL4APgAZf5l/l38XfzX+9f7Iv0i/bH/sf9QAlACvwO/A1MDUwNWAVYB0/7T/v38/fyX/Jf8nP2c/Xf/d/9TAVMBiQKJArMCswK/Ab8B6//r/9X91f1l/GX8ePx4/Fn+Wf5XAVcB+QP5A7kEuQQAAwADov+i/3T8dPw++z77mvya/LL/sv/GAsYCVwRXBOED4QP4AfgBof+h/7D9sP2M/Iz8Wvxa/ED9QP1E/0T//AH8AVkEWQQMBQwFYQNhA+f/5/9n/Gf83frd+jr8Ovyu/67/LgMuA7kEuQSZA5kDuAC4ANz93P2H/If8If0h/Qv/C/8uAS4BrgKuAhsDGwNnAmcCugC6AI7+jv7B/MH8Y/xj/BX+Ff5bAVsBmQSZBNIF0gUDBAME7f/t/9P70/sU+hT6pPuk+2//b/8bAxsDnQSdBHcDdwPeAN4Aqv6q/hH+Ef74/vj+XgBeAEABQAFFAUUBwgDCACgAKACp/6n/NP80/9n+2f7x/vH+3v/e/4QBhAEFAwUDMAMwA18BXwEt/i3+SvtK+4j6iPqc/Jz8nwCfAIkEiQRpBmkGfwV/BX4CfgLz/vP+UPxQ/Er7SvvM+8z7W/1b/Xn/ef+0AbQBigOKA2AEYATAA8ADtgG2Aff+9/6q/Kr81fvV+9z83PxF/0X/AQIBAuUD5QMvBC8ExgLGAjkAOQCO/Y793Pvc++j76PvB/cH9qACoAFkDWQOVBJUEywPLA1IBUgFK/kr++/v7+0/7T/t1/HX8+f75/gECAQKKBIoEqAWoBbkEuQS/Ab8Blf2V/df51/lE+ET40vnS+Qv+C/4VAxUDmwabBv8G/wY+BD4E2v/a//n7+ftA+kD6GfsZ+779vv3NAM0ACAMIA8QDxAP5AvkCIQEhAfj++P5J/Un9rfyt/FH9Uf3c/tz+lACUAMYBxgEcAhwCuwG7AfwA/AAoACgAR/9H/17+Xv6q/ar9rf2t/c7+zv7VANUAyQLJAmsDawMIAggCIf8h/zT8NPzq+ur6+Pv4+8r+yv7eAd4BzQPNA/wD/AO1ArUCqgCqAHP+c/6A/ID8UftR+5n7mfvN/c39hwGHAToFOgXLBssG4gTiBPz//P96+nr6SvdK9yr4KviO/I78DQINAu8F7wW7BrsGzQTNBJwBnAGe/p7+fPx8/Ej7SPsJ+wn7EPwQ/JP+k/4RAhECHgUeBRcGFwZDBEMEfwB/AMv8y/wM+wz7zfvN+xf+F/5MAEwAawFrAZoBmgGkAaQBBAIEAkkCSQKSAZIBif+J/wz9DP3B+8H72/zb/A0ADQCEA4QDIgUiBe8D7wPDAMMAof2h/Un8Sfwr/Sv9XP9c/4MBgwHJAskCMAMwAxUDFQOUApQCbQFtAXn/ef9E/UT9/Pv8+7v8u/yM/4z/IQMhA3sFewUzBTMFYQJhAoz+jP66+7r7Mvsy+/D88Pzd/93/ogKiAkQERARlBGUEIAMgA9gA2AA//j/+TfxN/P37/fu2/bb92gDaANwD3AMJBQkFmgOaAz0APQDJ/Mn8GPsY+/f79/u4/rj+yQHJAbcDtwPwA/AD0ALQAhkBGQFr/2v/Ev4S/jn9Of0i/SL9Ef4R/u//7/8TAhMCdAN0A0cDRwOXAZcBVf9V/8j9yP25/bn9+/77/p4AngCjAaMBpgGmAf4A/gBXAFcAGgAaACcAJwANAA0Ahf+F/77+vv5G/kb+lf6V/q7/rv8QARABDAIMAi8CLwJ3AXcBRQBFABH/Ef9C/kL+Ef4R/pr+mv7I/8j/SgFKAYgCiALhAuECAgICAj4APgB2/nb+pf2l/Tv+O/7E/8T/LAEsAXkBeQF8AHwA+v76/iD+IP6r/qv+XABcACACIALJAskC4wHjAfP/8/8R/hH+K/0r/XX9df2C/oL+s/+z/7oAugCbAZsBXgJeAq8CrwIAAgACAwADAD39Pf3/+v/6t/q3+vf89/zVANUAVgRWBI8FjwXbA9sDPwA/AMD8wPwl+yX76Pvo+yz+LP51AHUArwGvAa4BrgH/AP8AQQBBALD/sP8z/zP/vP68/ov+i/4E/wT/PQA9ALABsAFxAnEC0wHTAfL/8v/I/cj9ivyK/Ov86/yr/qv+0ADQAFICUgKqAqoC9gH2AasAqwA5/zn/6v3q/QP9A/3g/OD8zP3M/bL/sv/nAecBYgNiA1oDWgPIAcgBf/9//6D9oP3q/Or8Uv1S/UX+Rf44/zj/DwAPAPwA/AAMAgwCyQLJAngCeAK4ALgAEv4S/tj72Ptf+1/7Cv0K/QgACADAAsAC4wPjAxkDGQMTARMB2/7b/jT9NP1l/GX8ePx4/Iz9jP2u/67/egJ6AtME0wROBU4FHQMdA93+3f6U+pT6kviS+BD6EPpX/lf+NgM2A1UGVQaABoAGIAQgBJUAlQBO/U79H/sf+2D6YPo4+zj7uf25/XoBegE9BT0FLAcsB9oF2gV1AXUBDfwN/Jf4l/gj+SP5eP14/S0DLQMdBx0HXQddBzEEMQSn/6f/FfwV/M/6z/rB+8H7Bf4F/qsAqwAPAw8DrQStBOwE7ARZA1kDNwA3AMX8xfzK+sr6cvty+3H+cf4JAgkCKwQrBNUD1QOwAbABbv9u/3r+ev73/vf+6//r/zcANwCa/5r/0f7R/tD+0P7Y/9j/MwEzAdcB1wFRAVEBKAAoAFr/Wv94/3j/KQApAI8AjwAbABsAKP8o/6b+pv4+/z7/mwCbAKABoAFaAVoB6P/o/3n+ef5e/l7+4//j//kB+QH3AvcC7AHsAWP/Y/8N/Q39ePx4/P/9//2XAJcAuAK4AnEDcQPgAuACwwHDAaQApACB/4H/G/4b/qX8pfz6+/r7EP0Q/QgACACuA64DCAYIBo0FjQVPAk8CAf4B/vv6+/q7+rv6F/0X/YUAhQAmAyYD6APoA+AC4AL0APQAJP8k/xD+EP7j/eP9hv6G/rr/uv8cARwBJgImAlkCWQKNAY0BIwAjANH+0f49/j3+kv6S/nL/cv9KAEoAwQDBANsA2wDPAM8AtwC3AHEAcQDW/9b/Bf8F/3b+dv6p/qn+sv+y/w8BDwHsAewBvAG8AZkAmQBA/0D/f/5//rX+tf6d/53/mQCZACMBIwEVARUBkwCTANj/2P8V/xX/gf6B/nP+c/4v/y//nQCdAAwCDAKHAocCeAF4AUv/S/9V/VX9CP0I/dL+0v6lAaUBkAOQAxcDFwNiAGIANv02/cb7xvsh/SH9aQBpAHQDdANSBFIEnwKfAqf/p/9b/Vv95vzm/Av+C/6a/5r/hgCGAK4ArgCtAK0AFQEVAbcBtwHBAcEBiACIAFP+U/5f/F/8DPwM/NP90/3PAM8ASgNKA90D3QNUAlQCsv+y/3D9cP2T/JP8Mf0x/bf+t/5qAGoAzQHNAZ0CnQKgAqACoQGhAa3/rf9e/V79wfvB+9z73Pvn/ef9DAEMAbkDuQOFBIUEBwMHAwcABwAT/RP9jvuO+xH8Efwy/jL+5wDnAAsDCwPUA9QDBgMGA/gA+AB4/nj+jPyM/A/8D/xE/UT9of+h//wB/AEkAyQDkQKRArIAsgCv/q/+rv2u/Sj+KP6j/6P/HQEdAa4BrgESARIBuv+6/23+bf7O/c79Fv4W/hT/FP9eAF4AggGCAR0CHQLuAe4B7wDvAGf/Z//0/fT9RP1E/cf9x/1X/1f/QQFBAZYClgKwArACjAGMAcz/zP9a/lr+5P3k/Yn+if7N/83/8gDyAFwBXAHuAO4ACAAIAE//T/9B/0H/7P/s/+IA4gB+AX4BTgFOAWIAYgBK/0r/uv66/hD/EP8QABAACQEJAU0BTQG0ALQAtv+2/x//H/92/3b/mACYAMQBxAEeAh4CRQFFAZn/mf8C/gL+Yv1i/RD+EP6y/7L/gwGDAcICwgIGAwYDTwJPAt8A3wAi/yL/lP2U/bv8u/wE/QT9if6J/twA3AAUAxQDLQQtBJEDkQN8AXwB7v7u/iX9Jf3q/Or8JP4k/vD/8P88ATwBaQFpAawArADT/9P/qv+q/2cAZwB5AXkB6wHrARYBFgEp/yn/JP0k/Uj8SPxM/Uz93f/d/70CvQJ4BHgENAQ0BCcCJwJn/2f/P/0//X/8f/wq/Sr9rP6s/k8ATwCXAZcBVgJWAnsCewL7AfsB1wDXAFX/Vf8H/gf+l/2X/VL+Uv7i/+L/agFqAQsCCwKAAYABSABIAE7/Tv82/zb/6P/o/6QApACnAKcAxf/F/5n+mf4X/hf+z/7P/nwAfAAuAi4C6gLqAk4CTgLAAMAAE/8T//r9+v2y/bL9DP4M/sP+w/6v/6//wADAAMcBxwFgAmACIAIgAusA6wAs/yz/rv2u/Sb9Jv3Q/dD9TP9M/+4A7gAcAhwClQKVAlYCVgJ0AXQBBgAGAEP+Q/60/LT8G/wb/Az9DP1t/23/TAJMAkEEQQQ9BD0ENQI1AjT/NP/A/MD89fv1++/87/zf/t/+rwCvAK0BrQHMAcwBbQFtAe0A7QBeAF4AqP+o/8r+yv4M/gz+1v3W/WT+ZP6G/4b/vQC9AIMBgwGeAZ4BMAEwAYkAiQDl/+X/Vf9V/9T+1P5z/nP+W/5b/rH+sf5u/27/VABUABUBFQF7AXsBhgGGAUUBRQG/AL8A5f/l/7/+v/6c/Zz9DP0M/Zn9mf1U/1T/ogGiAXIDcgPMA8wDaQJpAt3/3f9R/VH91PvU++b75vtR/VH9cP9w/5YBlgE4AzgD+wP7A5MDkwPiAeIBKP8o/y/8L/wr+iv6MPow+pr8mvyaAJoAiQSJBJMGkwa1BbUFPwI/ArX9tf0b+hv6+fj5+Lj6uPp3/nf+iAKIAjYFNgWFBYUFlQOVA3gAeACl/aX9Pvw+/JL8kvwX/hf+0f/R//MA8wBIAUgBLQEtASEBIQFSAVIBeQF5AR4BHgESABIAsf6x/rX9tf22/bb9tf61/hwAHAAuAS4BkQGRAYYBhgGDAYMBuAG4AbYBtgHQANAAyf7J/l/8X/wP+w/7Jvwm/Lb/tv80BDQEPgc+B/4G/gZpA2kDUv5S/lX6VfpW+Vb5dPt0+zz/PP+uAq4CeQR5BHIEcgQ8AzwDjwGPAbz/vP/e/d79RvxG/LH7sfvT/NP8p/+n/wkDCQMxBTEFzgTOBP8B/wFh/mH+G/wb/Gf8Z/zP/s/+iAGIAcQCxALuAe4B/f/9/5X+lf6+/r7+KgAqAIcBhwGgAaABUwBTAKH+of7a/dr9h/6H/gwADAAvAS8BHQEdARQAFAAn/yf/T/9P/5kAmQAOAg4CcwJzAj0BPQEJ/wn/L/0v/dH80fwj/iP+XQBdAF0CXQJOA04DCgMKA9oB2gEuAC4AY/5j/un86fxZ/Fn8R/1H/cT/xP/0AvQCQwVDBS0FLQVXAlcC+P34/Vv6W/qN+Y35HPwc/L8AvwAEBQQFyQbJBlAFUAWQAZABd/13/d363fqn+qf6lvyW/KT/pP+NAo0CSARIBFEEUQS+Ar4COgA6AMX9xf1X/Ff8Zvxm/MT9xP21/7X/bAFsAWoCagKhAqECPgI+AmEBYQEZABkAjP6M/jj9OP3S/NL82P3Y/Q0ADQBlAmUCkgOSA94C3gK2ALYAb/5v/mb9Zv0T/hP+wf/B/yIBIgFNAU0BYgBiAFT/VP8d/x3/6//r/wcBBwF0AXQBxgDGAH3/ff+d/p3+2v7a/v7//v8WARYBMwEzAS0ALQDK/sr+Kf4p/uv+6/6pAKkAPwI/ApACkAJZAVkBVv9W/8/9z/25/bn9H/8f/xsBGwFtAm0CPQI9AqUApQCb/pv+Y/1j/cn9yf2f/5//0AHQAf4C/gJdAl0CNAA0AMP9w/2E/IT8PP08/X//f//zAfMBMwMzA6wCrALsAOwAJv8m/03+Tf6F/oX+Lv8u/5X/lf+N/43/hP+E/wMAAwAHAQcB4AHgAa8BrwE1ADUAM/4z/gP9A/2Y/Zj9vv++/yMCIwI5AzkDTgJOAgAAAADD/cP92PzY/ID9gP3//v/+TwBPAPEA8QAbARsBRwFHAY4BjgGAAYABjwCPAMP+w/77/Pv8a/xr/Lv9u/1jAGMA7QLtAuYD5gPVAtUCggCCAEz+TP5A/UD9dP10/Un+Sf4O/w7/nv+e/0IAQgAxATEBFgIWAjgCOAIcARwBHf8d/1/9X/0F/QX9Uf5R/lsAWwC4AbgBjQGNAT4APgAM/wz/FP8U/0sASwCPAY8BiAGIAeX/5f+2/bb9uPy4/O/97/3GAMYAWwNbA9YD1gPEAcQBbP5s/un76fu6+7r7yP3I/bQAtADUAtQCUwNTA2MCYwLSANIARf9F/wP+A/4v/S/9Kv0q/Wn+af7hAOEAmQOZA+wE7ASpA6kDHQAdADr8Ovxs+mz68fvx+9f/1/+QA5ADtwS3BMECwQJT/1P/8/zz/CP9I/1c/1z/ngGeASkCKQLNAM0A9P70/kn+Sf5V/1X/EQERAe0B7QEpASkBgP+A/3D+cP7n/uf+ZgBmAHsBewEUARQBh/+H/1X+Vf7U/tT+5wDnAO0C7QILAwsDvwC/AHj9eP2Q+5D7fvx+/LL/sv//Av8CSQRJBAgDCAOCAIIAnv6e/lH+Uf4a/xr/sv+y/2r/av/J/sn+/P78/okAiQCjAqMCpgOmA4YChgLM/8z/VP1U/dT81PyA/oD+2wDbAAACAAIvAS8Bav9q/4D+gP5w/3D/dQF1Aa8CrwLEAcQBKv8q/9v82/zD/MP8Ff8V/xQCFAJvA28DJwInAlf/V/9H/Uf9hf2F/aX/pf+yAbIB6QHpAUkASQCC/oL+dv52/nEAcQDGAsYCJAMkA58AnwCu/K78MPow+kT7RPuI/4j/SwRLBH0GfQbVBNUEpwCnALf8t/ws+yz7Nfw1/Fj+WP7w//D/gACAAMkAyQCwAbABGgMaA84DzgOPAo8CYP9g/9n72fso+ij6dvt2+w//D//SAtICuQS5BBwEHAThAeEBjf+N/x/+H/6h/aH9mP2Y/dD90P2S/pL+MAAwAFECUQLVA9UDigOKAy8BLwHk/eT9jvuO+4/7j/vX/df99QD1ACYDJgN9A30DUAJQAqsAqwBq/2r/sv6y/jD+MP6+/b79vP28/b7+vv7NAM0AEgMSAykEKQQoAygDVwBXACz9LP1h+2H71fvV+xf+F/7HAMcAkwKTAvcC9wJNAk0CRwFHAVUAVQB7/3v/nP6c/t393f2z/bP9gf6B/jIAMgAPAg8CJwMnA9UC1QI1ATUBCv8K/1D9UP3C/ML8kP2Q/Wn/af+dAZ0BXANcA+UD5QPVAtUCZwBnAIT9hP2A+4D7bvtu+4D9gP3NAM0AwQPBA/ME8wTuA+4DWAFYAX3+ff6R/JH8H/wf/AT9BP25/rn+swCzAHgCeAKQA5ADgwODAw4CDgKC/4L/2vza/GX7ZfsH/Af8nf6d/u0B7QFHBEcEeAR4BHICcgJT/1P/s/yz/ND70Pv4/Pj8hP+E/0cCRwIWBBYEPwQ/BLMCswIQABAAZf1l/d373fs1/DX8Xf5d/mUBZQHiA+IDqASoBGUDZQPSANIAOv46/sP8w/zb/Nv8Jv4m/uH/4f9hAWEBTgJOApQClAIyAjICLAEsAar/qv8m/ib+UP1Q/bL9sv04/zj/JAEkAWICYgI/Aj8C3gDeACj/KP8w/jD+ff59/rf/t//wAPAAUgFSAasAqwCG/4b/tP60/rn+uf59/33/cgByAAwBDAESARIBrwCvACoAKgCy/7L/S/9L/wL/Av8F/wX/lv+W/7IAsgDmAeYBbQJtAq4BrgHP/8//xf3F/dj82PzF/cX9JgAmAJoCmgKfA58DkwKTAiYAJgDh/eH9Hv0e/R/+H/4AAAAAbgFuAakBqQHwAPAALAAsAAoACgBtAG0AkwCTAOf/5/+f/p/+vv2+/Tf+N/4QABAAJAIkAvEC8QLFAcUBYv9i/4P9g/2G/Yb9WP9Y/3YBdgEfAh8CuQC5AGP+Y/4R/RH9Af4B/qkAqQAUAxQDXwNfAzcBNwEe/h7+SvxK/PT89Px8/3z/8wHzAakCqQJqAWoBgP+A/4j+iP4f/x//eQB5AC0BLQF6AHoA8P7w/vH98f19/n3+UgBSABYCFgJtAm0CCwELAfL+8v6h/aH99/33/Zf/l/9DAUMB2wHbARgBGAGk/6T/eP54/jL+Mv7M/sz+1v/W/9gA2AB/AX8BlwGXAfkA+QCv/6//J/4n/i39Lf2A/YD9OP84/4QBhAENAw0D1wLXAvkA+QCX/pf+Hv0e/UD9QP2X/pf+EAAQANUA1QDeAN4AvgC+AOYA5gAgASABuwC7AFj/WP+D/YP9iPyI/Hr9ev0uAC4AGQMZA0gESATLAssCdv92/1z8XPxj+2P7+fz5/On/6f9QAlAC+wL7AggCCAKIAIgAdP90/wj/CP/Z/tn+i/6L/lL+Uv7E/sT+IQAhANsB2wHPAs8CJAIkAg8ADwDS/dL91vzW/Kb9pv2V/5X/TAFMAd0B3QFWAVYBggCCABAAEAD8//z/tf+1/+b+5v75/fn97P3s/V7/Xv/NAc0BrwOvA4ADgAMFAQUBpv2m/Y77jvsi/CL8AP8A/0cCRwLtA+0DKwMrA9YA1gCb/pv+sP2w/SH+If4d/x3/3v/e/1IAUgDpAOkA3QHdAacCpwJbAlsCgwCDANr92v0I/Aj8gPyA/Ez/TP/RAtEC0wTTBAAEAATqAOoAm/2b/Sv8K/xM/Uz98P/w/zUCNQLEAsQCrAGsARcAFwA//z//f/9//zYANgCEAIQAFgAWAGr/av9L/0v/EAAQADcBNwHNAc0BNgE2Abf/t/9S/lL+B/4H/hT/FP/HAMcABQIFAhACEAIDAQMBqf+p/9n+2f7l/uX+ef95//3//f8YABgA7v/u/+z/7P9YAFgABQEFAWcBZwEMAQwB+//7/8/+z/5P/k/+5f7l/kgASACgAaABEgISAlcBVwHp/+n/t/63/n/+f/5Q/1D/ggCCADEBMQHhAOEA1//X/+X+5f7N/s3+sf+x//MA8wCqAaoBRwFHAQMAAwC1/rX+Ov46/tT+1P4EAAQA8gDyAA4BDgFzAHMAwP/A/4j/iP/d/93/RgBGADUANQCQ/5D/1/7X/sT+xP6l/6X/BwEHAe4B7gGUAZQBAwADAC3+Lf5F/UX97/3v/b7/vv+JAYkBMwIzAnUBdQH3//f/0P7Q/qn+qf5b/1v/IwAjAFIAUgDd/93/W/9b/3n/ef9YAFgAYQFhAbEBsQHVANUANf81/9793v2//b/9AP8A/9cA1wAXAhcC/QH9AbQAtAAn/yf/VP5U/pv+m/6W/5b/ewB7AMoAygCTAJMAUQBRAFoAWgCNAI0AbQBtALD/sP+c/pz+/v3+/YP+g/4cABwA3QHdAZUClQK0AbQBzP/M/zH+Mf4B/gH+SP9I//AA8AChAaEB0wDTAEL/Qv9a/lr+Cv8K//IA8gCbApsClgKWArEAsQAr/iv+0fzR/JX9lf3e/97/+QH5AXwCfAJPAU8Btf+1/x3/Hf/1//X/QgFCAY8BjwE6ADoAI/4j/gT9BP0J/gn+uAC4ADwDPAO9A70D2QHZAfL+8v4c/Rz9f/1//Yr/iv97AXsB4AHgAbEAsQA8/zz/6P7o/gAAAAB0AXQB0wHTAZEAkQCP/o/+X/1f/f79/v0BAAEA9AH0AYECgQJyAXIBuf+5/4z+jP52/nb+Ef8R/6P/o//Y/9j/CQAJAKUApQCNAY0B9AH0ARQBFAEE/wT/+vz6/Hn8efwh/iH+AQEBATQDNAM/Az8DNQE1AaD+oP5P/U/98P3w/a3/rf/7APsA9gD2AAcABwBn/2f/5f/l/xoBGgHDAcMB9AD0AAr/Cv9+/X79s/2z/bT/tP8SAhIC+AL4AqsBqwEo/yj/X/1f/ab9pv2r/6v/xAHEAUwCTAL5APkAAP8A/wn+Cf7K/sr+gACAAKkBqQFIAUgBuf+5/03+Tf4r/iv+Xf9d/9oA2gB0AXQB1ADUAKv/q/8G/wb/Wv9a/yoAKgCLAIsABgAGAA7/Dv+b/pv+SP9I/7kAuQDXAdcBqwGrAS8ALwBg/mD+fP18/Rr+Gv6//7//TQFNAdgB2AFDAUMBLQAtAFz/XP8q/yr/aP9o/6//r//J/8n/1v/W/w0ADQBwAHAAvAC8AKAAoAATABMAZv9m/xD/EP9P/0///P/8/6cApwDsAOwAqgCqAA8ADwBt/23/B/8H/wP/A/9m/2b/GgAaAOYA5gBtAW0BVgFWAYMAgwBC/0L/OP44/gL+Av7N/s3+LgAuAGQBZAHRAdEBXQFdAW4AbgCW/5b/Kv8q/yH/If9B/0H/aP9o/6D/oP8CAAIAegB6AMMAwwCfAJ8AGwAbAJb/lv95/3n/1P/U/0gASABSAFIAxv/G/w7/Dv/e/t7+m/+b/+oA6gDeAd4BoQGhASsAKwBh/mH+eP14/RX+Ff7Q/9D/gQGBARwCHAJmAWYBEQARABD/EP/k/uT+TP9M/6v/q/+p/6n/g/+D/8P/w/+fAJ8AngGeAeQB5AHqAOoADP8M/2z9bP0t/S39mv6a/uYA5gCwArAC8gLyAqQBpAGr/6v/Kv4q/s/9z/2E/oT+tv+2/8wAzAByAXIBlAGUATkBOQFxAHEAcP9w/6D+oP55/nn+Lf8t/2oAagB5AXkBtAG0Af4A/gDe/97/Gf8Z/xr/Gv+u/67/PQA9AFQAVAAGAAYAzv/O/xAAEACtAK0AFQEVAccAxwDT/9P/4P7g/qr+qv5l/2X/hQCFAC8BLwHpAOkABAAEAFD/UP9o/2j/KQApAM4AzgCgAKAAov+i/6X+pf6Y/pj+vP+8/1EBUQEiAiICegF6AcL/wv86/jr+Cf4J/lD/UP8VARUBAAIAAmgBaAHS/9L/ff59/mH+Yf54/3j/zwDPAF0BXQHUANQA0f/R/0b/Rv+k/6T/fwB/APoA+gCJAIkAev96/7b+tv74/vj+LAAsAHMBcwHSAdIB/wD/AJj/mP+o/qj+zv7O/sn/yf+9AL0A7wDvAF4AXgCx/7H/nv+e/zsAOwDxAPEA+QD5ABoAGgDl/uX+Uf5R/u3+7f5lAGUAtgG2AfQB9AH6APoAg/+D/5T+lP62/rb+nf+d/3cAdwCjAKMALQAtAK7/rv+4/7j/TQBNAN0A3QDGAMYA6v/q/97+3v52/nb+Hv8e/3UAdQCQAZABpQGlAaQApABE/0T/dP50/rf+t/7H/8f/2QDZADkBOQHBAMEA3//f/zn/Of8s/yz/o/+j/zsAOwCYAJgAnACcAGUAZQAfAB8A3//f/6b/pv94/3j/cP9w/6n/qf8hACEApACkAOUA5QC0ALQAHwAfAHL/cv8H/wf/E/8T/4f/h/8iACIAnACcAMMAwwCRAJEAJAAkAKv/q/9a/1r/VP9U/5z/nP8PAA8AbABsAHkAeQArACsAtP+0/2j/aP+F/4X/AgACAI8AjwDKAMoAgACAANP/0/8q/yr/5v7m/jD/MP/c/9z/kgCSAAABAAH+AP4AlQCVAOv/6/82/zb/tv62/qj+qP4w/zD/MQAxAEABQAHNAc0BbgFuATYANgC7/rv+0v3S/Qz+DP5S/1L/7gDuAPQB9AHUAdQBsACwAEH/Qf9b/lv+bP5s/kv/S/9gAGAAEgESARwBHAGhAKEA/////4v/i/9n/2f/hv+G/8r/yv8YABgAWgBaAHkAeQBiAGIAEgASAKr/qv9m/2b/eP94/+j/6P9+AH4A4ADgAMkAyQA8ADwAhP+E/w//D/8n/yf/xP/E/5EAkQAbARsBEwETAX8AfwC0/7T/Iv8i/xn/Gf+U/5T/SABIAMkAyQDRANEAYgBiAMX/xf9Z/1n/Wf9Z/7j/uP8zADMAewB7AGgAaAARABEAsf+x/4D/gP+R/5H/zP/M/w0ADQA5ADkAUABQAFUAVQBGAEYAFAAUALz/vP9b/1v/Kv8q/1z/XP/y//L/pwCnABMBEwHoAOgALQAtAEH/Qf+n/qf+sv6y/lb/Vv8xADEAzgDOAOsA6wCUAJQAEgASAKv/q/98/3z/eP94/4P/g/+U/5T/t/+3//n/+f9LAEsAggCCAHYAdgAfAB8Aqf+p/1f/V/9Y/1j/qP+o/xMAEwBcAFwAYgBiADQANAD7//v/2//b/9f/1//d/93/2f/Z/8v/y//H/8f/3v/e/wgACAAtAC0AMAAwAA0ADQDd/93/xv/G/9v/2/8NAA0ANQA1ADQANAAIAAgA0f/R/7v/u//a/9r/GwAbAFEAUQBVAFUAIQAhANT/1P+c/5z/nP+c/9P/0/8fAB8AWQBZAGcAZwBNAE0AHwAfAPj/+P/n/+f/6f/p//j/+P8KAAoAHQAdADAAMAA/AD8AQgBCADEAMQAPAA8A6f/p/9b/1v/m/+b/FgAWAEkASQBeAF4AQABAAPn/+f+y/7L/mv+a/8z/zP85ADkAqACoANgA2AClAKUAIgAiAI7/jv83/zf/TP9M/8P/w/9fAF8A0wDTAOwA7AClAKUAKgAqALb/tv95/3n/gv+C/77/vv8NAA0AUABQAHQAdAB2AHYAWgBaACYAJgDn/+f/rv+u/5T/lP+p/6n/7f/t/0kASQCRAJEAngCeAGIAYgD4//j/lv+W/3L/cv+h/6H/CQAJAG4AbgCYAJgAcgByABgAGAC//7//lv+W/6j/qP/f/9//EwATAC4ALgAzADMAMwAzADoAOgA/AD8ALAAsAPf/9/+z/7P/hf+F/5H/kf/X/9f/MwAzAHEAcQBsAGwALAAsANv/2/+p/6n/rP+s/9X/1f8EAAQAHAAcABwAHAATABMAEgASABkAGQAZABkABQAFAN7/3v+4/7j/rP+s/8T/xP/1//X/JAAkADoAOgAwADAADQANAOT/5P/E/8T/uv+6/8n/yf/w//D/JQAlAFIAUgBeAF4APgA+APv/+/+1/7X/k/+T/6n/qf/q/+r/MQAxAFgAWABOAE4AIgAiAPP/8//Y/9j/1v/W/+H/4f/p/+n/7v/u//v/+/8VABUAMwAzAD4APgAlACUA7P/s/7L/sv+b/5v/u/+7/wMAAwBIAEgAZABkAEoASgAQABAA3P/c/8r/yv/d/93//f/9/w8ADwAIAAgA8f/x/+D/4P/j/+P/9v/2/woACgAOAA4A/////+X/5f/O/87/xv/G/8//z//j/+P/+f/5/w4ADgAcABwAHwAfABAAEADv/+//wv/C/5//n/+c/5z/xv/G/xAAEABYAFgAdAB0AE0ATQDv/+//iv+K/1L/Uv9n/2f/v/+//yoAKgBvAG8AbwBvADMAMwDi/+L/qP+o/5v/m/+x/7H/0v/S/+3/7f8BAAEAFgAWADAAMABCAEIAOAA4AAkACQDG/8b/k/+T/5L/kv/I/8j/GAAYAFUAVQBeAF4ANgA2APz//P/T/9P/zP/M/9r/2v/n/+f/5v/m/+H/4f/p/+n/CQAJAC0ALQA5ADkAFwAXANP/0/+X/5f/kv+S/9L/0v85ADkAiwCLAJUAlQBNAE0A3f/d/4b/hv95/3n/uv+6/yEAIQBzAHMAiQCJAGAAYAAXABcA2P/Y/7z/vP/F/8X/4P/g//r/+v8MAAwAGwAbAC0ALQBAAEAASABIADkAOQAQABAA3//f/7//v//F/8X/7v/u/yYAJgBOAE4AUQBRADQANAALAAsA7//v/+f/5//u/+7/9f/1//j/+P/+//7/EwATADYANgBVAFUAUwBTACIAIgDR/9H/jP+M/33/ff+2/7b/HAAcAHkAeQCdAJ0AeAB4ACQAJADO/87/nP+c/5n/mf+7/7v/7v/u/yQAJABSAFIAbgBuAGcAZwA0ADQA4//j/5X/lf93/3f/oP+g/wAAAABjAGMAkgCSAHgAeAAqACoA3P/c/7z/vP/R/9H/AQABACYAJgAsACwAHAAcAA4ADgASABIAIQAhACcAJwAVABUA8f/x/9L/0v/Q/9D/7f/t/xcAFwA1ADUAOgA6ACcAJwALAAsA7f/t/9D/0P+y/7L/m/+b/57/nv/F/8X/CQAJAEoASgBfAF8AMwAzANb/1v96/3r/UP9Q/3D/cP/I/8j/KQApAGYAZgBqAGoAPwA/AAIAAgDO/87/tP+0/7X/tf/M/8z/8P/w/xkAGQA4ADgAQQBBAC4ALgAFAAUA2v/a/8L/wv/M/8z/9f/1/y0ALQBdAF0AcABwAF0AXQAqACoA6//r/77/vv+6/7r/5//n/zMAMwB5AHkAlACUAHUAdQAsACwA4//j/8H/wf/S/9L/BAAEADMAMwBGAEYAOgA6ACUAJQAcABwAIgAiACkAKQAgACAAAwADAOb/5v/f/9//9v/2/x0AHQA5ADkANgA2AB0AHQADAAMA/////w0ADQAYABgACwALAOT/5P++/77/uf+5/+L/4v8iACIAUABQAE0ATQAXABcA0P/Q/6L/ov+j/6P/yf/J//f/9/8SABIAFAAUAAgACAD8//z/8//z/+b/5v/S/9L/vf+9/7j/uP/Q/9D//f/9/ycAJwA0ADQAGQAZAOf/5/+8/7z/sP+w/8b/xv/t/+3/DQANABgAGAAQABAA/v/+/+v/6//a/9r/zP/M/8X/xf/N/83/6f/p/xEAEQAyADIANwA3ABgAGADk/+T/t/+3/6r/qv/E/8T/9P/0/yAAIAAzADMAKAAoAAwADADw//D/3f/d/9j/2P/b/9v/5P/k//P/8/8FAAUAFQAVABoAGgAPAA8A9v/2/93/3f/S/9L/3P/c//j/+P8VABUAIwAjABoAGgAAAAAA5P/k/9X/1f/b/9v/8f/x/woACgAaABoAGwAbABAAEAABAAEA9//3//X/9f/3//f/+f/5//n/+f/4//j//v/+/wwADAAcABwAJQAlAB0AHQAEAAQA5//n/9b/1v/d/93/+f/5/xsAGwAwADAALgAuABkAGQACAAIA9v/2//n/+f8DAAMADAAMAA0ADQALAAsADQANABUAFQAcABwAGgAaAA4ADgD/////9//3//7//v8PAA8AHQAdABsAGwAKAAoA9v/2/+7/7v/6//r/EQARACIAIgAgACAACwALAPP/8//o/+j/8P/w/wQABAASABIADwAPAP/////t/+3/6P/o//X/9f8LAAsAHQAdAB4AHgALAAsA7f/t/9T/1P/K/8r/1f/V//D/8P8QABAAJAAkACMAIwALAAsA5v/m/8j/yP/C/8L/2P/Y//////8hACEALAAsABoAGgD4//j/2v/a/9H/0f/g/+D/+//7/xQAFAAhACEAIAAgABQAFAAEAAQA8v/y/+H/4f/Y/9j/3v/e//f/9/8ZABkANgA2ADoAOgAgACAA9P/0/83/zf/B/8H/1P/U//v/+/8gACAAMQAxACkAKQATABMA+//7/+v/6//m/+b/6P/o/+3/7f/1//X//f/9/wUABQAJAAkACgAKAAcABwAEAAQAAgACAP/////5//n/8f/x/+r/6v/u/+7//v/+/xMAEwAhACEAHAAcAAcABwDs/+z/3//f/+n/6f8CAAIAGwAbACQAJAAZABkABAAEAPT/9P/x//H/+P/4/wAAAAADAAMAAgACAAUABQANAA0AFQAVABMAEwADAAMA6v/q/9n/2f/e/97/+v/6/x0AHQAzADMAMAAwABcAFwD3//f/4v/i/97/3v/k/+T/7f/t//b/9v8CAAIAFAAUACkAKQAzADMAJwAnAAQABADa/9r/wP/A/8b/xv/q/+r/GgAaADwAPABBAEEAKQApAAQABADj/+P/0v/S/9X/1f/m/+b//////xgAGAAoACgAKgAqABoAGgD+//7/5P/k/9n/2f/k/+T//v/+/xcAFwAjACMAHQAdAAsACwD5//n/7v/u/+r/6v/o/+j/6f/p/+//7/8AAAAAGwAbADMAMwA2ADYAHQAdAPH/8f/I/8j/uv+6/9L/0v8CAAIAMAAwAEQARAA2ADYAEQARAO3/7f/Y/9j/1//X/+L/4v/x//H//////wwADAAZABkAIgAiACEAIQARABEA+P/4/+D/4P/Y/9j/5f/l/wEAAQAeAB4ALgAuACgAKAASABIA9v/2/+L/4v/e/97/6//r/wMAAwAbABsAKQApACYAJgATABMA+v/6/+b/5v/h/+H/7f/t/wMAAwAZABkAJgAmACcAJwAfAB8AEQARAP/////t/+3/3v/e/93/3f/t/+3/DQANADAAMABEAEQAOwA7ABcAFwDs/+z/0P/Q/9L/0v/t/+3/DQANACAAIAAfAB8AEgASAAkACQAKAAoAEAAQAAwADAD5//n/3//f/9H/0f/f/9//BgAGADIAMgBGAEYANgA2AAsACwDf/9//y//L/9T/1P/v/+//BgAGAAwADAAGAAYAAgACAAkACQAVABUAGgAaAAkACQDo/+j/yf/J/8H/wf/a/9r/BwAHAC8ALwA8ADwAKQApAAEAAQDb/9v/x//H/8v/y//f/9//+P/4/w4ADgAdAB0AIQAhABoAGgAGAAYA6f/p/9D/0P/H/8f/2P/Y//z//P8jACMAOQA5ADEAMQAOAA4A4v/i/8L/wv+9/73/0//T//n/+f8dAB0AMwAzADEAMQAbABsA9//3/9T/1P+9/73/vP+8/9L/0v/5//n/IQAhADkAOQA4ADgAHAAcAPL/8v/M/8z/uv+6/8L/wv/g/+D/BgAGACUAJQAyADIAKwArABMAEwDz//P/2f/Z/83/zf/U/9T/6//r/wsACwAlACUAMQAxACYAJgAKAAoA5//n/8r/yv/B/8H/z//P//D/8P8WABYAMgAyADcANwAkACQAAQABANz/3P/H/8f/yv/K/+X/5f8LAAsALQAtAD0APQA2ADYAGwAbAPj/+P/b/9v/z//P/9j/2P/x//H/EAAQACoAKgA1ADUALQAtABcAFwD8//z/5//n/97/3v/k/+T/9f/1/wwADAAgACAALAAsACsAKwAdAB0ABgAGAO7/7v/d/93/3f/d/+3/7f8IAAgAIgAiAC0ALQAlACUADgAOAPT/9P/j/+P/4P/g/+v/6//8//z/CwALABQAFAAWABYAEwATAAwADAABAAEA9f/1/+v/6//q/+r/9P/0/wYABgAYABgAHgAeABUAFQAAAAAA6//r/+H/4f/o/+j/+f/5/w0ADQAXABcAFQAVAAgACAD6//r/8P/w//D/8P/3//f/AgACAAoACgAMAAwACAAIAAEAAQD6//r/9v/2//b/9v/6//r//f/9/wAAAAAAAAAAAAAAAAEAAQACAAIAAgACAAAAAAD8//z/+v/6//r/+v//////BAAEAAYABgADAAMA/P/8//X/9f/1//X/+v/6/wEAAQAFAAUAAgACAPr/+v/0//T/8//z//r/+v8DAAMACAAIAAUABQD+//7/9//3//X/9f/6//r/AQABAAUABQAEAAQAAAAAAP7//v8AAAAAAwADAAQABAABAAEA+f/5//T/9P/2//b/AQABAA8ADwAZABkAFwAXAAoACgD3//f/6P/o/+T/5P/s/+z//f/9/w0ADQAXABcAGAAYABEAEQAFAAUA+f/5//D/8P/s/+z/7v/u//b/9v8DAAMADwAPABgAGAAYABgAEQARAAUABQD6//r/8v/y//D/8P/z//P/+v/6/wQABAAPAA8AGQAZAB8AHwAbABsADQANAPf/9//k/+T/3f/d/+b/5v/9//3/FQAVACMAIwAfAB8ADQANAPf/9//n/+f/5f/l/+z/7P/4//j/AgACAAoACgARABEAFQAVABIAEgAFAAUA7//v/9z/3P/Z/9n/7P/s/w4ADgAsACwAMgAyABkAGQDv/+//zP/M/8b/xv/h/+H/DAAMAC4ALgA0ADQAHwAfAAAAAADp/+n/5P/k/+7/7v/7//v/AwADAAYABgALAAsAEgASABkAGQAWABYABgAGAO3/7f/a/9r/2v/a//D/8P8RABEAKAAoACkAKQATABMA9P/0/9z/3P/X/9f/5P/k//v/+/8PAA8AGgAaABkAGQAPAA8AAgACAPX/9f/r/+v/6P/o/+3/7f/6//r/CQAJABUAFQAYABgADwAPAAEAAQDx//H/6P/o/+b/5v/t/+3/+v/6/wsACwAZABkAIQAhAB0AHQAOAA4A9//3/+T/5P/e/97/6f/p/wAAAAAYABgAJgAmACIAIgARABEA/P/8/+7/7v/r/+v/8f/x//r/+v8CAAIACAAIAA0ADQARABEAEwATAA4ADgADAAMA9f/1/+7/7v/y//L/AQABABAAEAAWABYADgAOAPz//P/v/+//8P/w//////8TABMAHQAdABQAFAD9//3/5//n/97/3v/n/+f//P/8/w4ADgAVABUAEAAQAAYABgD/////+//7//j/+P/y//L/7P/s/+v/6//1//X/CAAIABgAGAAaABoACgAKAPH/8f/g/+D/4f/h//L/8v8GAAYAEAAQAAoACgD8//z/8v/y//T/9P/+//7/BgAGAAQABAD8//z/9v/2//v/+/8JAAkAEwATAA4ADgD5//n/4//j/9z/3P/s/+z/CwALACQAJAAmACYAEAAQAPH/8f/e/97/4f/h//T/9P8HAAcADwAPAAoACgAFAAUABwAHAA8ADwATABMACQAJAPP/8//f/9//3v/e//b/9v8ZABkAMgAyADAAMAAUABQA7//v/9f/1//Z/9n/7//v/wgACAAYABgAGQAZABIAEgAKAAoAAgACAPr/+v/w//D/5v/m/+T/5P/w//D/BgAGABsAGwAgACAAEQARAPX/9f/d/93/1P/U/9//3//2//b/DAAMABgAGAAYABgADgAOAAAAAADz//P/6P/o/+P/4//o/+j/9//3/wsACwAcABwAIQAhABUAFQD/////6v/q/+L/4v/q/+r//P/8/w8ADwAYABgAFQAVAAoACgD+//7/9v/2//T/9P/3//f//f/9/wIAAgAFAAUABQAFAAQABAABAAEA//////7//v/+//7//v/+//7//v//////AgACAAcABwAMAAwADAAMAAQABAD2//b/6v/q/+j/6P/0//T/CAAIABoAGgAfAB8AFQAVAAAAAADt/+3/5P/k/+n/6f/2//b/BAAEAA0ADQARABEAEQARAA0ADQAGAAYA+v/6/+z/7P/j/+P/5v/m//b/9v8MAAwAHgAeACEAIQATABMA/P/8/+j/6P/h/+H/6v/q//3//f8PAA8AFgAWABEAEQADAAMA9v/2//L/8v/4//j/BAAEAA4ADgASABIADAAMAAIAAgD6//r/+f/5/wAAAAALAAsAEgASABIAEgALAAsAAQABAPv/+//7//v/AAAAAAYABgAKAAoADAAMAAwADAAKAAoABwAHAAIAAgD//////v/+/wIAAgAIAAgADQANAAwADAAFAAUA+v/6//T/9P/3//f/AgACAA0ADQARABEACQAJAPv/+//x//H/8v/y//3//f8KAAoAEQARAA8ADwAFAAUA+//7//f/9//4//j/+//7//7//v8BAAEABgAGAAsACwANAA0ABwAHAPr/+v/s/+z/5//n//D/8P8EAAQAFwAXAB4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CHECKPOINT_PATH=$CHECKPOINT_PATH -e FACE=$FACE -e AUDIO=$AUDIO -e FACESIZE=$FACESIZE -e OUTFILE=$OUTFILE -e GFPGAN_MODEL_VERSION=$GFPGAN_MODEL_VERSION -e UPSCALE_FACTOR=$UPSCALE_FACTOR -e FPS=$FPS --cap-add=sys_nice --net=host --ipc=host opea/animation:test diff --git a/comps/animation/entrypoint.sh b/comps/animation/entrypoint.sh new file mode 100644 index 0000000000..f7a507de38 --- /dev/null +++ b/comps/animation/entrypoint.sh @@ -0,0 +1,22 @@ +#!/bin/sh +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +# Environment variables +export PT_HPU_LAZY_MODE=0 +export PT_HPU_ENABLE_REFINE_DYNAMIC_SHAPES=1 + +# Wav2Lip, GFPGAN +python3 animation.py \ +--port $((ANIMATION_PORT)) \ +--inference_mode $INFERENCE_MODE \ +--checkpoint_path $CHECKPOINT_PATH \ +--face $FACE \ +--audio $AUDIO \ +--outfile $OUTFILE \ +--img_size $((FACESIZE)) \ +-v $GFPGAN_MODEL_VERSION \ +-s $((UPSCALE_FACTOR)) \ +--fps $((FPS)) \ +--only_center_face \ +--bg_upsampler None diff --git 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index 0000000000..54eac78bec --- /dev/null +++ b/comps/animation/requirements.txt @@ -0,0 +1,30 @@ +basicsr==1.4.2 +docarray[full] +facexlib==0.3.0 +fastapi +ffmpeg-python +gradio==4.38.1 +gradio_client==1.1.0 +grpcio==1.64.1 +huggingface-hub==0.24.0 +librosa==0.10.2 +lmdb==1.5.1 +numba==0.60.0 +numpy==1.23.5 +opencv-python==4.10.0.84 + +# used by cloud native +opentelemetry-api +opentelemetry-exporter-otlp +opentelemetry-sdk +pandas +prometheus_fastapi_instrumentator +pyarrow +pydantic #==1.10.13 +pyyaml==6.0 +scipy==1.14.0 +shortuuid +tavily-python +tb-nightly==2.18.0a20240721 +tqdm==4.66.4 +yapf==0.40.2 diff --git a/comps/animation/test_animation_server.py b/comps/animation/test_animation_server.py new file mode 100644 index 0000000000..4c9e96ecd2 --- /dev/null +++ b/comps/animation/test_animation_server.py @@ -0,0 +1,21 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +import json +import os + +import requests + +ip_address = os.environ.get("ip_address") +endpoint = f"http://{ip_address}:7860/v1/animation" +outfile = os.environ.get("OUTFILE") + +# Read the JSON file +with open("assets/audio/sample_question.json", "r") as file: + data = json.load(file) + +response = requests.post(url=endpoint, json=data, headers={"Content-Type": "application/json"}, proxies={"http": None}) +print(f"Status code: {response.status_code}") +if response.status_code == 200: + print(f"Check {outfile} for the result.") +print(response.json()) diff --git a/comps/animation/utils.py b/comps/animation/utils.py new file mode 100644 index 0000000000..418c62f21b --- /dev/null +++ b/comps/animation/utils.py @@ -0,0 +1,367 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 +# Author: Chun Tao +# Date: 2024-08-02 + +# %% Imports +import argparse + +# audio +import base64 +import io +import os +from glob import glob + +# wav2lip +from os import listdir, path + +import cv2 + +# habana +import habana_frameworks.torch.core as htcore +import habana_frameworks.torch.hpu as hthpu +import numpy as np +import scipy +import soundfile as sf +import torch +import Wav2Lip.audio as audio +import Wav2Lip.face_detection as face_detection + +# gfpgan +from basicsr.utils import imwrite +from gfpgan import GFPGANer +from tqdm import tqdm +from Wav2Lip.models import Wav2Lip + +device = "hpu" if hthpu.is_available() else "cpu" +print("Using {} for inference.".format(device)) + + +def get_args(): + parser = argparse.ArgumentParser(description="Inference code to lip-sync videos in the wild using Wav2Lip models") + # General config + parser.add_argument("--port", type=int, default=7860) + parser.add_argument( + "--inference_mode", + type=str, + choices=["wav2clip_only", "wav2clip+gfpgan"], + default="wav2clip+gfpgan", + help="whether to use just wav2clip or include gfpgan", + ) + # Wav2Lip config + parser.add_argument( + "--checkpoint_path", type=str, help="Name of saved checkpoint to load weights from", required=True + ) + parser.add_argument("--face", type=str, help="Filepath of video/image that contains faces to use", required=True) + parser.add_argument( + "--audio", + type=str, + default="None", + help="Filepath of video/audio file to use as raw audio source", + required=False, + ) + parser.add_argument( + "--outfile", + type=str, + help="Video path to save result. See default for an e.g.", + default="results/result_voice.mp4", + ) + parser.add_argument( + "--static", type=bool, help="If True, then use only first video frame for inference", default=False + ) + parser.add_argument( + "--fps", + type=float, + help="Can be specified only if input is a static image (default: 25)", + default=25.0, + required=False, + ) + parser.add_argument( + "--pads", + nargs="+", + type=int, + default=[0, 10, 0, 0], + help="Padding (top, bottom, left, right). Please adjust to include chin at least", + ) + parser.add_argument("--face_det_batch_size", type=int, help="Batch size for face detection", default=16) + parser.add_argument("--wav2lip_batch_size", type=int, help="Batch size for Wav2Lip model(s)", default=128) + parser.add_argument( + "--resize_factor", + default=1, + type=int, + help="Reduce the resolution by this factor. Sometimes, best results are obtained at 480p or 720p", + ) + parser.add_argument( + "--crop", + nargs="+", + type=int, + default=[0, -1, 0, -1], + help="Crop video to a smaller region (top, bottom, left, right). Applied after resize_factor and rotate arg. " + "Useful if multiple face present. -1 implies the value will be auto-inferred based on height, width", + ) + parser.add_argument( + "--box", + nargs="+", + type=int, + default=[-1, -1, -1, -1], + help="Specify a constant bounding box for the face. Use only as a last resort if the face is not detected." + "Also, might work only if the face is not moving around much. Syntax: (top, bottom, left, right).", + ) + parser.add_argument( + "--rotate", + default=False, + action="store_true", + help="Sometimes videos taken from a phone can be flipped 90deg. If true, will flip video right by 90deg." + "Use if you get a flipped result, despite feeding a normal looking video", + ) + parser.add_argument( + "--nosmooth", + default=False, + action="store_true", + help="Prevent smoothing face detections over a short temporal window", + ) + # GFPGAN + parser.add_argument( + "-i", "--input", type=str, default="inputs/whole_imgs", help="Input image or folder. Default: inputs/whole_imgs" + ) + parser.add_argument("-o", "--output", type=str, default="results", help="Output folder. Default: results") + # we use version to select models, which is more user-friendly + parser.add_argument("--img_size", type=int, default=96, help="size to reshape the detected face") + parser.add_argument( + "-v", "--version", type=str, default="1.3", help="GFPGAN model version. Option: 1 | 1.2 | 1.3. Default: 1.3" + ) + parser.add_argument( + "-s", "--upscale", type=int, default=2, help="The final upsampling scale of the image. Default: 2" + ) + parser.add_argument( + "--bg_upsampler", type=str, default="realesrgan", help="background upsampler. Default: realesrgan" + ) + parser.add_argument( + "--bg_tile", + type=int, + default=400, + help="Tile size for background sampler, 0 for no tile during testing. Default: 400", + ) + parser.add_argument("--suffix", type=str, default=None, help="Suffix of the restored faces") + parser.add_argument("--only_center_face", action="store_true", help="Only restore the center face") + parser.add_argument("--aligned", action="store_true", help="Input are aligned faces") + parser.add_argument("--save_faces", default=False, help="Save the restored faces") + parser.add_argument( + "--ext", + type=str, + default="auto", + help="Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto", + ) + args = parser.parse_args() + + if os.path.isfile(args.face) and args.face.split(".")[1] in ["jpg", "png", "jpeg"]: + args.static = True + os.makedirs(args.output, exist_ok=True) + + return args + + +# %% Custom functions +def get_smoothened_boxes(boxes, T): + for i in range(len(boxes)): + if i + T > len(boxes): + window = boxes[len(boxes) - T :] + else: + window = boxes[i : i + T] + boxes[i] = np.mean(window, axis=0) + return boxes + + +def face_detect(args, images): + detector = face_detection.FaceAlignment(face_detection.LandmarksType._2D, flip_input=False, device=device) + + batch_size = args.face_det_batch_size + + while 1: + predictions = [] + try: + for i in tqdm(range(0, len(images), batch_size)): + predictions.extend(detector.get_detections_for_batch(np.array(images[i : i + batch_size]))) + except RuntimeError: + if batch_size == 1: + raise RuntimeError( + "Image too big to run face detection on GPU. Please use the --resize_factor argument" + ) + batch_size //= 2 + print("Recovering from OOM error; New batch size: {}".format(batch_size)) + continue + break + + results = [] + pady1, pady2, padx1, padx2 = args.pads + for rect, image in zip(predictions, images): + if rect is None: + cv2.imwrite("temp/faulty_frame.jpg", image) # check this frame where the face was not detected. + raise ValueError("Face not detected! Ensure the video contains a face in all the frames.") + + y1 = max(0, rect[1] - pady1) + y2 = min(image.shape[0], rect[3] + pady2) + x1 = max(0, rect[0] - padx1) + x2 = min(image.shape[1], rect[2] + padx2) + + results.append([x1, y1, x2, y2]) + + boxes = np.array(results) + if not args.nosmooth: + boxes = get_smoothened_boxes(boxes, T=5) + results = [[image[y1:y2, x1:x2], (y1, y2, x1, x2)] for image, (x1, y1, x2, y2) in zip(images, boxes)] + + del detector + return results + + +def datagen(args, frames, mels): + img_batch, mel_batch, frame_batch, coords_batch = [], [], [], [] + + if args.box[0] == -1: + if not args.static: + face_det_results = face_detect(args, frames) # BGR2RGB for CNN face detection + else: + face_det_results = face_detect(args, [frames[0]]) + else: + print("Using the specified bounding box instead of face detection...") + y1, y2, x1, x2 = args.box + face_det_results = [[f[y1:y2, x1:x2], (y1, y2, x1, x2)] for f in frames] + + for i, m in enumerate(mels): + idx = 0 if args.static else i % len(frames) + frame_to_save = frames[idx].copy() + face, coords = face_det_results[idx].copy() + + face = cv2.resize(face, (args.img_size, args.img_size)) + + img_batch.append(face) + mel_batch.append(m) + frame_batch.append(frame_to_save) + coords_batch.append(coords) + + if len(img_batch) >= args.wav2lip_batch_size: + img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch) + + img_masked = img_batch.copy() + img_masked[:, args.img_size // 2 :] = 0 + + img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.0 + mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1]) + + yield img_batch, mel_batch, frame_batch, coords_batch + img_batch, mel_batch, frame_batch, coords_batch = [], [], [], [] + + if len(img_batch) > 0: + img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch) + + img_masked = img_batch.copy() + img_masked[:, args.img_size // 2 :] = 0 + + img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.0 + mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1]) + + yield img_batch, mel_batch, frame_batch, coords_batch + + +def _load(checkpoint_path): + if device == "cuda": + checkpoint = torch.load(checkpoint_path) + else: + checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage) + return checkpoint + + +def load_model(path): + model = Wav2Lip() + print("Load checkpoint from: {}".format(path)) + checkpoint = _load(path) + s = checkpoint["state_dict"] + new_s = {} + for k, v in s.items(): + new_s[k.replace("module.", "")] = v + model.load_state_dict(new_s) + return model.eval().to(device) + + +def load_bg_upsampler(args): + if (not torch.cuda.is_available()) or (not hthpu.is_available()): # CPU + import warnings + + warnings.warn( + "The unoptimized RealESRGAN is slow on CPU. We do not use it. " + "If you really want to use it, please modify the corresponding codes." + ) + bg_upsampler = None + else: + from basicsr.archs.rrdbnet_arch import RRDBNet + from realesrgan import RealESRGANer + + model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2) + bg_upsampler = RealESRGANer( + scale=2, + model_path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth", + model=model, + tile=args.bg_tile, + tile_pad=10, + pre_pad=0, + half=True, + ) # need to set False in CPU mode + return bg_upsampler.eval().to(device) + + +def load_gfpgan(args, bg_upsampler): + if args.version == "1": + arch = "original" + channel_multiplier = 1 + model_name = "GFPGANv1" + elif args.version == "1.2": + arch = "clean" + channel_multiplier = 2 + model_name = "GFPGANCleanv1-NoCE-C2" + elif args.version == "1.3": + arch = "clean" + channel_multiplier = 2 + model_name = "GFPGANv1.3" + else: + raise ValueError(f"Wrong model version {args.version}.") + + # determine model path + model_path = path.join( + "/usr/local/lib/python3.10/dist-packages/gfpgan/experiments/pretrained_models", model_name + ".pth" + ) + if not path.isfile(model_path): + model_path = path.join("usr/local/lib/python3.10/dist-packages/gfpgan/realesrgan/weights", model_name + ".pth") + if not path.isfile(model_path): + raise ValueError(f"Model {model_name} does not exist") + + restorer = GFPGANer( + model_path=model_path, + upscale=args.upscale, + arch=arch, + channel_multiplier=channel_multiplier, + bg_upsampler=bg_upsampler, + device=device, + ) + + # torch.compile + # restorer.face_helper.face_det = torch.compile(restorer.face_helper.face_det, backend="hpu_backend") + # restorer.face_helper.face_parse = torch.compile(restorer.face_helper.face_parse, backend="hpu_backend") + # restorer.gfpgan = torch.compile(restorer.gfpgan, backend="hpu_backend") # some compilation issue + # print("Model GFPGAN and face helper compiled") + + return restorer + + +def base64_to_int16_to_wav(base64_string, output_wav_file): + """Convert base64 string to int16 numpy array and save as .wav file.""" + # Decode the base64 string to binary data + wav_bytes = base64.b64decode(base64_string) + + # Read the binary data using soundfile + buf = io.BytesIO(wav_bytes) + y, sr = sf.read(buf, dtype="int16") + + # Write the binary data to a .wav file + sf.write(output_wav_file, y, sr, format="WAV") + return sr, y From d85f33a3946b474f4e83be3b3bf8089b97e319ce Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Tue, 8 Oct 2024 03:10:38 +0000 Subject: [PATCH 03/49] Revert "updates needed for demo" This reverts commit f0c7a026305ace410610c9dba771699e13dde8ea. Signed-off-by: Chun Tao --- comps/cores/mega/gateway.py | 6 ++---- comps/tts/speecht5/dependency/speecht5_model.py | 3 --- 2 files changed, 2 insertions(+), 7 deletions(-) diff --git a/comps/cores/mega/gateway.py b/comps/cores/mega/gateway.py index 79b29006d2..9eda239038 100644 --- a/comps/cores/mega/gateway.py +++ b/comps/cores/mega/gateway.py @@ -419,10 +419,8 @@ async def handle_request(self, request: Request): ) last_node = runtime_graph.all_leaves()[-1] - #response = result_dict[last_node]["byte_str"] - response = {"byte_str": result_dict[last_node]["byte_str"], - "text": result_dict["llm/MicroService"]["text"], - "query": result_dict["asr/MicroService"]["query"]} + response = result_dict[last_node]["byte_str"] + return response diff --git a/comps/tts/speecht5/dependency/speecht5_model.py b/comps/tts/speecht5/dependency/speecht5_model.py index 20ae8f0d40..8a800d68d8 100644 --- a/comps/tts/speecht5/dependency/speecht5_model.py +++ b/comps/tts/speecht5/dependency/speecht5_model.py @@ -51,9 +51,6 @@ def __init__(self, device="cpu"): def split_long_text_into_batch(self, text, batch_length=128): """Batch the long text into sequences of shorter sentences.""" - # ctao - Replace AI with "A, I" - text = text.replace("AI", "A, I") - text = text.replace("OPEX", "OPEA") res = [] hitted_ends = [",", ".", "?", "!", "。", ";", " "] idx = 0 From a8363b7d17d7d8b7a65c8d6602f7b08b567f8fca Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Tue, 8 Oct 2024 03:24:06 +0000 Subject: [PATCH 04/49] remove improper images Signed-off-by: Chun Tao --- comps/animation/assets/pallavi.png | Bin 116360 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 comps/animation/assets/pallavi.png diff --git a/comps/animation/assets/pallavi.png b/comps/animation/assets/pallavi.png deleted file mode 100644 index fdc4444a429e4812927cca1777eec99333352577..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 116360 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e900fb0ac3..d9ef21d2bf 100644 --- a/comps/animation/README.md +++ b/comps/animation/README.md @@ -23,7 +23,7 @@ export FACE='assets/avatar1.jpg' # export AUDIO='assets/eg3_ref.wav' # audio file path is optional, will use base64str as input if is 'None' export AUDIO='None' export FACESIZE=96 -export OUTFILE='/home/demo/ctao/forks/GenAIComps/comps/animation/outputs/result.mp4' +export OUTFILE="$(pwd)/comps/animation/outputs/result.mp4" export GFPGAN_MODEL_VERSION=1.3 export UPSCALE_FACTOR=1 export FPS=10 @@ -34,7 +34,7 @@ export FPS=10 ```bash -docker run --privileged -d --runtime=habana --cap-add=sys_nice --net=host --ipc=host --name "animation-service" -v /var/run/docker.sock:/var/run/docker.sock -v /usr/bin/docker:/usr/bin/docker -v $(pwd):$(pwd) -w /home/user/comps/animation -e HABANA_VISIBLE_DEVICES="3" -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e PYTHON=/usr/bin/python3.10 -e INFERENCE_MODE=$INFERENCE_MODE -e CHECKPOINT_PATH=$CHECKPOINT_PATH -e FACE=$FACE -e AUDIO=$AUDIO -e FACESIZE=$FACESIZE -e OUTFILE=$OUTFILE -e GFPGAN_MODEL_VERSION=$GFPGAN_MODEL_VERSION -e UPSCALE_FACTOR=$UPSCALE_FACTOR -e FPS=$FPS -e ANIMATION_PORT=$ANIMATION_PORT opea/animation:latest +docker run --privileged -d --runtime=habana --cap-add=sys_nice --net=host --ipc=host --name "animation-service" -v /var/run/docker.sock:/var/run/docker.sock -v /usr/bin/docker:/usr/bin/docker -v $(pwd):$(pwd) -w /home/user/comps/animation -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e PYTHON=/usr/bin/python3.10 -e INFERENCE_MODE=$INFERENCE_MODE -e CHECKPOINT_PATH=$CHECKPOINT_PATH -e FACE=$FACE -e AUDIO=$AUDIO -e FACESIZE=$FACESIZE -e OUTFILE=$OUTFILE -e GFPGAN_MODEL_VERSION=$GFPGAN_MODEL_VERSION -e UPSCALE_FACTOR=$UPSCALE_FACTOR -e FPS=$FPS -e ANIMATION_PORT=$ANIMATION_PORT opea/animation:latest ``` # 🚀2. Start Microservice with Python (option 2) @@ -44,7 +44,7 @@ Follow 1.1 and 1.2 steps from the above section to build the Docker image and se ## 2.1. Run the Docker container by overriding the entrypoint ```bash -docker run --privileged --rm -it --runtime=habana --cap-add=sys_nice --net=host --ipc=host --name "animation-service" -v /var/run/docker.sock:/var/run/docker.sock -v /usr/bin/docker:/usr/bin/docker -v $(pwd):$(pwd) -w /home/user/comps/animation -e HABANA_VISIBLE_DEVICES="3" -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e PYTHON=/usr/bin/python3.10 -e INFERENCE_MODE=$INFERENCE_MODE -e CHECKPOINT_PATH=$CHECKPOINT_PATH -e FACE=$FACE -e AUDIO=$AUDIO -e FACESIZE=$FACESIZE -e OUTFILE=$OUTFILE -e GFPGAN_MODEL_VERSION=$GFPGAN_MODEL_VERSION -e UPSCALE_FACTOR=$UPSCALE_FACTOR -e FPS=$FPS -e ANIMATION_PORT=$ANIMATION_PORT --entrypoint /bin/bash opea/animation:latest +docker run --privileged --rm -it --runtime=habana --cap-add=sys_nice --net=host --ipc=host --name "animation-service" -v /var/run/docker.sock:/var/run/docker.sock -v /usr/bin/docker:/usr/bin/docker -v $(pwd):$(pwd) -w /home/user/comps/animation -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e PYTHON=/usr/bin/python3.10 -e INFERENCE_MODE=$INFERENCE_MODE -e CHECKPOINT_PATH=$CHECKPOINT_PATH -e FACE=$FACE -e AUDIO=$AUDIO -e FACESIZE=$FACESIZE -e OUTFILE=$OUTFILE -e GFPGAN_MODEL_VERSION=$GFPGAN_MODEL_VERSION -e UPSCALE_FACTOR=$UPSCALE_FACTOR -e FPS=$FPS -e ANIMATION_PORT=$ANIMATION_PORT --entrypoint /bin/bash opea/animation:latest ``` ## 2.2 Inside the container, run the following command to start the microservice diff --git a/comps/animation/assets/flowchart.png b/comps/animation/assets/flowchart.png index 565b09a2270570544c28a9c548d619c91350a076..b1da170f085e4bcc64fe894d8f8388c7e377558c 100644 GIT binary patch literal 123738 zcmeFZ2T)UM+ct`wvJsUgARw|uilB7q7zC8w0@4+P&;n9IZz`Zt6=|Ud2!s+4NkWHh zp-2h6g{C4kgx>2}yzdVB&bPla=bSm`pYzT9YsQh4tgNT3`@XKbuGczRsTUKFsi?pg2T6xOdmU+hTt7oWK6$LDRBJ?1J)8_07=dH(Aar8jSe4 z)DDCOO~cGfhKXZ8#@Na$Y8PlvW?fa6hy$z9ApLSRS2|YLa+q#*C-g~VY!c&BQm0c> 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z4>{n+LGU=doPTs+cs?yK{6e^ga77GU0WS#51puh%@Qc7RgWxW!L6q>q;F;mc08j<| ztpxy(<=}A%cpTn40|#({=LfOF!+W@F!p^)P#L#5`6c+=45>x?DGF(#C0T8SK0A+Fm zpsW-Cl)VKnNdR8r008FQ1Hc0409cG30E_DbU3xL(}0I&u(0JdNN zz_vdCup2y||1|)PYX`t-U;vzJ3V>^)0C4L(0Pcg25l1`V`8H?)@NXpmd`Sp^AIAU) z8U_Ht`v5@5S^)^n3jp$}0f6xH0T3~G)n()Wh;k(W(QpPJnu2gP5&-0lI{?xB13>f% z;GK!{Kg~xDlXZg-z&7k4`k)<{kUiuLC<&He4-o;qfsNTilt5q@p##K_GRMQy1Kx7~ zzThqUKXU(tVAw|oh$AL0{1DzL{d)=zhT{lV{gd+lsc2y_|ESl0C2)S+&G{BxEv7xH!K0*4hH}{fB?_4Tmpc% zDgp3O5CFc0Q$tt)Am|eS1pg7fA;vraVZ4D`8Jw)ZI-G0^oN7D(Q8|E9C4*C?0U+8e Z07NGrfat-~>EFQ4Tp@z6Id90@{{uJ7nY{o2 From dc59a77d637bc9d61262b40636aa9e085fdf7714 Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Wed, 9 Oct 2024 16:52:42 +0000 Subject: [PATCH 06/49] Add Dockerfile for cpu support Signed-off-by: Chun Tao --- comps/animation/Dockerfile | 84 ++++++++++++++++++++++++++++++++++ comps/animation/Dockerfile_hpu | 1 + comps/animation/animation.py | 17 +++++-- comps/animation/entrypoint.sh | 1 + comps/animation/utils.py | 26 +++++------ 5 files changed, 111 insertions(+), 18 deletions(-) create mode 100644 comps/animation/Dockerfile diff --git a/comps/animation/Dockerfile b/comps/animation/Dockerfile new file mode 100644 index 0000000000..97299a71b5 --- /dev/null +++ b/comps/animation/Dockerfile @@ -0,0 +1,84 @@ +# Use a base image +FROM python:3.11-slim + +# Set environment variables +ENV LANG=en_US.UTF-8 +ENV PYTHONPATH=/home:/home/user +ENV PYTHON=/usr/bin/python3.11 + +# Install dependencies +RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \ + yasm \ + libx264-dev + +# TO-DO: Add a user for security + +# Install GenAIComps +RUN mkdir -p /home/user/comps +COPY comps /home/user/comps +COPY comps/animation/entrypoint.sh /usr/local/bin/entrypoint.sh + +# Install ppmpeg with x264 software codec +RUN git clone https://github.com/FFmpeg/FFmpeg.git /home/user/comps/animation/FFmpeg +WORKDIR /home/user/comps/animation/FFmpeg +RUN ./configure --enable-gpl --enable-libx264 && \ + make -j$(nproc-1) && \ + make install && \ + hash -r +RUN chmod +x $(which ffmpeg) + +# # Install Wav2Lip-GFPGAN from github +# RUN git clone https://github.com/ajay-sainy/Wav2Lip-GFPGAN.git /home/user/comps/animation/src +# # Rename subfolders "Wav2Lip" and "GFPGAN" +# RUN mv /home/user/comps/animation/src/Wav2Lip-master /home/user/comps/animation/src/Wav2Lip && \ +# mv /home/user/comps/animation/src/GFPGAN-master /home/user/comps/animation/src/GFPGAN + +# # Set the PYTHONPATH environment variable +# RUN touch /home/user/comps/animation/src/Wav2Lip/__init__.py +# RUN touch /home/user/comps/animation/src/GFPGAN/__init__.py +# ENV PYTHONPATH="/home/user/comps/animation/src:/home/user/comps/animation/src/Wav2Lip:/home/user/comps/animation/src/GFPGAN:$PYTHONPATH" + +# Upgrade pip +RUN python3 -m pip install --upgrade pip + +# Install Wav2Lip from pip +RUN pip install --no-deps Wav2Lipy +RUN rm /usr/local/lib/python3.10/dist-packages/Wav2Lip/__init__.py && touch /usr/local/lib/python3.10/dist-packages/Wav2Lip/__init__.py +ENV PYTHONPATH="/usr/local/lib/python3.10/dist-packages/Wav2Lip:$PYTHONPATH" + +# Install GFPGAN from pip +RUN pip install --no-deps gfpgan +RUN touch /usr/local/lib/python3.10/dist-packages/gfpgan/__init__.py +ENV PYTHONPATH="/usr/local/lib/python3.10/dist-packages/gfpgan:$PYTHONPATH" + +# Download pre-trained models +# Install gdown +RUN pip install gdown +WORKDIR /usr/local/lib/python3.10/dist-packages +# RUN wget https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth -O Wav2Lip/face_detection/detection/sfd/s3fd.pth +RUN gdown https://drive.google.com/uc?id=1wwts8U4Wx2lpLOI9uGcUB-TLmKRIFnP8 -O Wav2Lip/face_detection/detection/sfd/s3fd.pth +RUN mkdir -p Wav2Lip/checkpoints +RUN gdown https://drive.google.com/uc?id=1mIKfu_onFKbkbrq6cKVRBblXe5bNMhd9 -O Wav2Lip/checkpoints/wav2lip.pth +RUN gdown https://drive.google.com/uc?id=1DD7dtUfNWqNoW-2Gnp78B4PyaiJ5ybhP -O Wav2Lip/checkpoints/wav2lip_gan.pth +RUN wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P gfpgan/experiments/pretrained_models + +# Install pip dependencies +RUN pip install -r /home/user/comps/animation/requirements.txt + +# Custom patches +# Modify the degradations.py file to import rgb_to_grayscale from torchvision.transforms.functional +RUN sed -i 's/from torchvision.transforms.functional_tensor import rgb_to_grayscale/from torchvision.transforms.functional import rgb_to_grayscale/' /usr/local/lib/python3.10/dist-packages/basicsr/data/degradations.py + +# Modify the core.py file to include 'hpu' in the device check +RUN sed -i "s/if 'cpu' not in device and 'cuda' not in device:/if 'cpu' not in device and 'cuda' not in device and 'hpu' not in device:/" /usr/local/lib/python3.10/dist-packages/Wav2Lip/face_detection/detection/core.py + +# To be compatible with librosa==0.10.2, instead of librosa==0.7.0 because the co-dependency numba==0.48 cannot be pip installed +RUN sed -i 's/hp.sample_rate, hp.n_fft/sr=hp.sample_rate, n_fft=hp.n_fft/' /usr/local/lib/python3.10/dist-packages/Wav2Lip/audio.py + +# Set the working directory +WORKDIR /home/user/comps/animation + +# Define the command to run when the container starts +RUN chmod +x /usr/local/bin/entrypoint.sh +ENV DEVICE="cpu" +ENTRYPOINT ["/usr/local/bin/entrypoint.sh"] \ No newline at end of file diff --git a/comps/animation/Dockerfile_hpu b/comps/animation/Dockerfile_hpu index 3ed0447851..a5a12cbe13 100644 --- a/comps/animation/Dockerfile_hpu +++ b/comps/animation/Dockerfile_hpu @@ -81,4 +81,5 @@ WORKDIR /home/user/comps/animation # Define the command to run when the container starts RUN chmod +x /usr/local/bin/entrypoint.sh +ENV DEVICE="hpu" ENTRYPOINT ["/usr/local/bin/entrypoint.sh"] \ No newline at end of file diff --git a/comps/animation/animation.py b/comps/animation/animation.py index b6923a4e1a..c45735adbc 100644 --- a/comps/animation/animation.py +++ b/comps/animation/animation.py @@ -41,12 +41,21 @@ statistics_dict, ) -device = "hpu" if hthpu.is_available() else "cpu" -print("Using {} for inference.".format(device)) - args = get_args() print("args: ", args) +# Specify device +if args.device == "hpu" and hthpu.is_available(): + device = "hpu" +elif args.device == "cuda": + device = "cuda" +elif args.device == "cpu": + device = "cpu" +else: + device = "cpu" + print("Invalid device argument, fall back to cpu") +print("Using {} for inference.".format(device)) + # Register the microservice @register_microservice( @@ -144,7 +153,7 @@ def animate(input: Base64ByteStrDoc): ): if i == 0: # load Wav2Lip model - model = load_model(args.checkpoint_path) + model = load_model(args) print("Wav2Lip Model loaded") # load BG sampler if needed diff --git a/comps/animation/entrypoint.sh b/comps/animation/entrypoint.sh index f7a507de38..e615506afc 100644 --- a/comps/animation/entrypoint.sh +++ b/comps/animation/entrypoint.sh @@ -8,6 +8,7 @@ export PT_HPU_ENABLE_REFINE_DYNAMIC_SHAPES=1 # Wav2Lip, GFPGAN python3 animation.py \ +--device $DEVICE \ --port $((ANIMATION_PORT)) \ --inference_mode $INFERENCE_MODE \ --checkpoint_path $CHECKPOINT_PATH \ diff --git a/comps/animation/utils.py b/comps/animation/utils.py index 418c62f21b..f87a7dfc35 100644 --- a/comps/animation/utils.py +++ b/comps/animation/utils.py @@ -33,13 +33,11 @@ from tqdm import tqdm from Wav2Lip.models import Wav2Lip -device = "hpu" if hthpu.is_available() else "cpu" -print("Using {} for inference.".format(device)) - def get_args(): parser = argparse.ArgumentParser(description="Inference code to lip-sync videos in the wild using Wav2Lip models") # General config + parser.add_argument("--device", type=str, choices=["hpu", "cpu", "cuda"], default="cpu") parser.add_argument("--port", type=int, default=7860) parser.add_argument( "--inference_mode", @@ -173,7 +171,7 @@ def get_smoothened_boxes(boxes, T): def face_detect(args, images): - detector = face_detection.FaceAlignment(face_detection.LandmarksType._2D, flip_input=False, device=device) + detector = face_detection.FaceAlignment(face_detection.LandmarksType._2D, flip_input=False, device=args.device) batch_size = args.face_det_batch_size @@ -264,24 +262,24 @@ def datagen(args, frames, mels): yield img_batch, mel_batch, frame_batch, coords_batch -def _load(checkpoint_path): - if device == "cuda": - checkpoint = torch.load(checkpoint_path) +def _load(args): + if args.device == "cuda": + checkpoint = torch.load(args.checkpoint_path) else: - checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage) + checkpoint = torch.load(args.checkpoint_path, map_location=lambda storage, loc: storage) return checkpoint -def load_model(path): +def load_model(args): model = Wav2Lip() - print("Load checkpoint from: {}".format(path)) - checkpoint = _load(path) + print("Load checkpoint from: {}".format(args.checkpoint_path)) + checkpoint = _load(args.checkpoint_path) s = checkpoint["state_dict"] new_s = {} for k, v in s.items(): new_s[k.replace("module.", "")] = v model.load_state_dict(new_s) - return model.eval().to(device) + return model.eval().to(args.device) def load_bg_upsampler(args): @@ -307,7 +305,7 @@ def load_bg_upsampler(args): pre_pad=0, half=True, ) # need to set False in CPU mode - return bg_upsampler.eval().to(device) + return bg_upsampler.eval().to(args.device) def load_gfpgan(args, bg_upsampler): @@ -341,7 +339,7 @@ def load_gfpgan(args, bg_upsampler): arch=arch, channel_multiplier=channel_multiplier, bg_upsampler=bg_upsampler, - device=device, + device=args.device, ) # torch.compile From bf38204afcee24b8d7c419c88fdd0c03b789b321 Mon Sep 17 00:00:00 2001 From: "Hoong Tee, Yeoh" Date: Tue, 8 Oct 2024 14:12:39 +0800 Subject: [PATCH 07/49] CODEOWNER: Update comp CODEOWNER (#757) Signed-off-by: Yeoh, Hoong Tee Signed-off-by: Chun Tao --- .github/CODEOWNERS | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index 9e794dbc1b..cabc758c9d 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -14,7 +14,9 @@ /comps/lvms/ sihan.chen@intel.com /comps/nginx/ letong.han@intel.com /comps/prompt_registry/ hoong.tee.yeoh@intel.com +/comps/feedback_management/ hoong.tee.yeoh@intel.com /comps/chathistory/ yogesh.pandey@intel.com +/comps/texttosql/ yogesh.pandey@intel.com /comps/reranks/ xuhui.ren@intel.com /comps/retrievers/ xuhui.ren@intel.com /comps/tts/ sihan.chen@intel.com From b932b947c720acc198aa5eebc7696a4f957bd3ac Mon Sep 17 00:00:00 2001 From: XinyuYe-Intel Date: Tue, 8 Oct 2024 14:13:52 +0800 Subject: [PATCH 08/49] Add stable diffusion microservice (#729) * add stable diffusion microservice. Signed-off-by: Ye, Xinyu * added test. Signed-off-by: Ye, Xinyu * changed output to images bytes data Signed-off-by: Ye, Xinyu * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * unified inference and wrapper into one microservice. Signed-off-by: Ye, Xinyu * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix test. Signed-off-by: Ye, Xinyu --------- Signed-off-by: Ye, Xinyu Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Chun Tao --- .../docker/compose/text2image-compose-cd.yaml | 14 ++ comps/__init__.py | 2 + comps/cores/proto/docarray.py | 9 ++ comps/text2image/Dockerfile | 23 ++++ comps/text2image/Dockerfile.intel_hpu | 30 +++++ comps/text2image/README.md | 97 ++++++++++++++ comps/text2image/__init__.py | 2 + comps/text2image/requirements.txt | 15 +++ comps/text2image/text2image.py | 123 ++++++++++++++++++ tests/text2image/test_text2image.sh | 59 +++++++++ 10 files changed, 374 insertions(+) create mode 100644 .github/workflows/docker/compose/text2image-compose-cd.yaml create mode 100644 comps/text2image/Dockerfile create mode 100644 comps/text2image/Dockerfile.intel_hpu create mode 100644 comps/text2image/README.md create mode 100644 comps/text2image/__init__.py create mode 100644 comps/text2image/requirements.txt create mode 100644 comps/text2image/text2image.py create mode 100644 tests/text2image/test_text2image.sh diff --git a/.github/workflows/docker/compose/text2image-compose-cd.yaml b/.github/workflows/docker/compose/text2image-compose-cd.yaml new file mode 100644 index 0000000000..d2737922f4 --- /dev/null +++ b/.github/workflows/docker/compose/text2image-compose-cd.yaml @@ -0,0 +1,14 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +# this file should be run in the root of the repo +# images used by GenAIExamples: text2image,text2image-gaudi +services: + text2image: + build: + dockerfile: comps/text2image/Dockerfile + image: ${REGISTRY:-opea}/text2image:${TAG:-latest} + text2image-gaudi: + build: + dockerfile: comps/text2image/Dockerfile.intel_hpu + image: ${REGISTRY:-opea}/text2image-gaudi:${TAG:-latest} diff --git a/comps/__init__.py b/comps/__init__.py index 753ac35985..46f5f78e09 100644 --- a/comps/__init__.py +++ b/comps/__init__.py @@ -26,6 +26,8 @@ ImagesPath, VideoPath, ImageDoc, + SDInputs, + SDOutputs, TextImageDoc, MultimodalDoc, EmbedMultimodalDoc, diff --git a/comps/cores/proto/docarray.py b/comps/cores/proto/docarray.py index 3e4b2ed921..3b6ce50215 100644 --- a/comps/cores/proto/docarray.py +++ b/comps/cores/proto/docarray.py @@ -284,6 +284,15 @@ class LVMVideoDoc(BaseDoc): max_new_tokens: conint(ge=0, le=1024) = 512 +class SDInputs(BaseDoc): + prompt: str + num_images_per_prompt: int = 1 + + +class SDOutputs(BaseDoc): + images: list + + class ImagePath(BaseDoc): image_path: str diff --git a/comps/text2image/Dockerfile b/comps/text2image/Dockerfile new file mode 100644 index 0000000000..50dc6e3b7f --- /dev/null +++ b/comps/text2image/Dockerfile @@ -0,0 +1,23 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +FROM python:3.11-slim + +# Set environment variables +ENV LANG=en_US.UTF-8 + +ARG ARCH="cpu" + +COPY comps /home/comps + +RUN pip install --no-cache-dir --upgrade pip && \ + if [ ${ARCH} = "cpu" ]; then pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu; fi && \ + pip install --no-cache-dir -r /home/comps/text2image/requirements.txt + +ENV PYTHONPATH=$PYTHONPATH:/home + +WORKDIR /home/comps/text2image + +RUN echo python text2image.py --bf16 >> run.sh + +CMD bash run.sh \ No newline at end of file diff --git a/comps/text2image/Dockerfile.intel_hpu b/comps/text2image/Dockerfile.intel_hpu new file mode 100644 index 0000000000..ffa228cf81 --- /dev/null +++ b/comps/text2image/Dockerfile.intel_hpu @@ -0,0 +1,30 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +# HABANA environment +# FROM vault.habana.ai/gaudi-docker/1.16.1/ubuntu22.04/habanalabs/pytorch-installer-2.2.2:latest as hpu +FROM opea/habanalabs:1.16.1-pytorch-installer-2.2.2 as hpu +RUN useradd -m -s /bin/bash user && \ + mkdir -p /home/user && \ + chown -R user /home/user/ + +COPY comps /home/user/comps + +RUN chown -R user /home/user/comps/text2image + +RUN rm -rf /etc/ssh/ssh_host* +USER user +# Set environment variables +ENV LANG=en_US.UTF-8 +ENV PYTHONPATH=/home/user:/usr/lib/habanalabs/:/home/user/optimum-habana + +# Install requirements and optimum habana +RUN pip install --no-cache-dir --upgrade pip && \ + pip install --no-cache-dir -r /home/user/comps/text2image/requirements.txt && \ + pip install --no-cache-dir optimum[habana] + +WORKDIR /home/user/comps/text2image + +RUN echo python text2image.py --device hpu --use_hpu_graphs --bf16 >> run.sh + +CMD bash run.sh \ No newline at end of file diff --git a/comps/text2image/README.md b/comps/text2image/README.md new file mode 100644 index 0000000000..e4eadc3cf7 --- /dev/null +++ b/comps/text2image/README.md @@ -0,0 +1,97 @@ +# Text-to-Image Microservice + +Text-to-Image is a task that generate image conditioning on the provided text. This microservice supports text-to-image task by using Stable Diffusion (SD) model. + +# 🚀1. Start Microservice with Python (Option 1) + +## 1.1 Install Requirements + +```bash +pip install -r requirements.txt +``` + +## 1.2 Start Text-to-Image Microservice + +Select Stable Diffusion (SD) model and assign its name to a environment variable as below: + +```bash +# SD1.5 +export MODEL=stable-diffusion-v1-5/stable-diffusion-v1-5 +# SD2.1 +export MODEL=stabilityai/stable-diffusion-2-1 +# SDXL +export MODEL=stabilityai/stable-diffusion-xl-base-1.0 +# SD3 +export MODEL=stabilityai/stable-diffusion-3-medium-diffusers +``` + +Set huggingface token: + +```bash +export HF_TOKEN= +``` + +Start the OPEA Microservice: + +```bash +python text2image.py --bf16 --model_name_or_path $MODEL --token $HF_TOKEN +``` + +# 🚀2. Start Microservice with Docker (Option 2) + +## 2.1 Build Images + +Select Stable Diffusion (SD) model and assign its name to a environment variable as below: + +```bash +# SD1.5 +export MODEL=stable-diffusion-v1-5/stable-diffusion-v1-5 +# SD2.1 +export MODEL=stabilityai/stable-diffusion-2-1 +# SDXL +export MODEL=stabilityai/stable-diffusion-xl-base-1.0 +# SD3 +export MODEL=stabilityai/stable-diffusion-3-medium-diffusers +``` + +### 2.1.1 Text-to-Image Service Image on Xeon + +Build text-to-image service image on Xeon with below command: + +```bash +cd ../.. +docker build -t opea/text2image:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/text2image/Dockerfile . +``` + +### 2.1.2 Text-to-Image Service Image on Gaudi + +Build text-to-image service image on Gaudi with below command: + +```bash +cd ../.. +docker build -t opea/text2image-gaudi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/text2image/Dockerfile.intel_hpu . +``` + +## 2.2 Start Text-to-Image Service + +### 2.2.1 Start Text-to-Image Service on Xeon + +Start text-to-image service on Xeon with below command: + +```bash +docker run --ipc=host -p 9379:9379 -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e HF_TOKEN=$HF_TOKEN -e MODEL=$MODEL opea/text2image:latest +``` + +### 2.2.2 Start Text-to-Image Service on Gaudi + +Start text-to-image service on Gaudi with below command: + +```bash +docker run -p 9379:9379 --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e HF_TOKEN=$HF_TOKEN -e MODEL=$MODEL opea/text2image-gaudi:latest +``` + +# 3 Test Text-to-Image Service + +```bash +http_proxy="" curl http://localhost:9379/v1/text2image -XPOST -d '{"prompt":"An astronaut riding a green horse", "num_images_per_prompt":1}' -H 'Content-Type: application/json' +``` diff --git a/comps/text2image/__init__.py b/comps/text2image/__init__.py new file mode 100644 index 0000000000..916f3a44b2 --- /dev/null +++ b/comps/text2image/__init__.py @@ -0,0 +1,2 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 diff --git a/comps/text2image/requirements.txt b/comps/text2image/requirements.txt new file mode 100644 index 0000000000..f639b4ba13 --- /dev/null +++ b/comps/text2image/requirements.txt @@ -0,0 +1,15 @@ +accelerate +datasets +diffusers +docarray[full] +fastapi +opentelemetry-api +opentelemetry-exporter-otlp +opentelemetry-sdk +prometheus-fastapi-instrumentator +pydantic==2.7.2 +pydub +shortuuid +torch +transformers +uvicorn diff --git a/comps/text2image/text2image.py b/comps/text2image/text2image.py new file mode 100644 index 0000000000..050df49d56 --- /dev/null +++ b/comps/text2image/text2image.py @@ -0,0 +1,123 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +import argparse +import base64 +import os +import threading +import time + +import torch +from diffusers import DiffusionPipeline + +from comps import ( + CustomLogger, + SDInputs, + SDOutputs, + ServiceType, + opea_microservices, + register_microservice, + register_statistics, + statistics_dict, +) + +logger = CustomLogger("text2image") +pipe = None +args = None +initialization_lock = threading.Lock() +initialized = False + + +def initialize(): + global pipe, args, initialized + with initialization_lock: + if not initialized: + # initialize model and tokenizer + if os.getenv("MODEL", None): + args.model_name_or_path = os.getenv("MODEL") + kwargs = {} + if args.bf16: + kwargs["torch_dtype"] = torch.bfloat16 + if not args.token: + args.token = os.getenv("HF_TOKEN") + if args.device == "hpu": + kwargs.update( + { + "use_habana": True, + "use_hpu_graphs": args.use_hpu_graphs, + "gaudi_config": "Habana/stable-diffusion", + "token": args.token, + } + ) + if "stable-diffusion-3" in args.model_name_or_path: + from optimum.habana.diffusers import GaudiStableDiffusion3Pipeline + + pipe = GaudiStableDiffusion3Pipeline.from_pretrained( + args.model_name_or_path, + **kwargs, + ) + elif "stable-diffusion" in args.model_name_or_path.lower() or "flux" in args.model_name_or_path.lower(): + from optimum.habana.diffusers import AutoPipelineForText2Image + + pipe = AutoPipelineForText2Image.from_pretrained( + args.model_name_or_path, + **kwargs, + ) + else: + raise NotImplementedError( + "Only support stable-diffusion, stable-diffusion-xl, stable-diffusion-3 and flux now, " + + f"model {args.model_name_or_path} not supported." + ) + elif args.device == "cpu": + pipe = DiffusionPipeline.from_pretrained(args.model_name_or_path, token=args.token, **kwargs) + else: + raise NotImplementedError(f"Only support cpu and hpu device now, device {args.device} not supported.") + logger.info("Stable Diffusion model initialized.") + initialized = True + + +@register_microservice( + name="opea_service@text2image", + service_type=ServiceType.TEXT2IMAGE, + endpoint="/v1/text2image", + host="0.0.0.0", + port=9379, + input_datatype=SDInputs, + output_datatype=SDOutputs, +) +@register_statistics(names=["opea_service@text2image"]) +def text2image(input: SDInputs): + initialize() + start = time.time() + prompt = input.prompt + num_images_per_prompt = input.num_images_per_prompt + + generator = torch.manual_seed(args.seed) + images = pipe(prompt, generator=generator, num_images_per_prompt=num_images_per_prompt).images + image_path = os.path.join(os.getcwd(), prompt.strip().replace(" ", "_").replace("/", "")) + os.makedirs(image_path, exist_ok=True) + results = [] + for i, image in enumerate(images): + save_path = os.path.join(image_path, f"image_{i+1}.png") + image.save(save_path) + with open(save_path, "rb") as f: + bytes = f.read() + b64_str = base64.b64encode(bytes).decode() + results.append(b64_str) + statistics_dict["opea_service@text2image"].append_latency(time.time() - start, None) + return SDOutputs(images=results) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--model_name_or_path", type=str, default="stabilityai/stable-diffusion-3-medium-diffusers") + parser.add_argument("--use_hpu_graphs", default=False, action="store_true") + parser.add_argument("--device", type=str, default="cpu") + parser.add_argument("--token", type=str, default=None) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--bf16", action="store_true") + + args = parser.parse_args() + + logger.info("Text2image server started.") + opea_microservices["opea_service@text2image"].start() diff --git a/tests/text2image/test_text2image.sh b/tests/text2image/test_text2image.sh new file mode 100644 index 0000000000..0872e6cb58 --- /dev/null +++ b/tests/text2image/test_text2image.sh @@ -0,0 +1,59 @@ +#!/bin/bash +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +set -x + +WORKPATH=$(dirname "$PWD") +ip_address=$(hostname -I | awk '{print $1}') + +function build_docker_images() { + cd $WORKPATH + echo $(pwd) + docker build --no-cache -t opea/text2image:latest -f comps/text2image/Dockerfile . + if [ $? -ne 0 ]; then + echo "opea/text2image built fail" + exit 1 + else + echo "opea/text2image built successful" + fi +} + +function start_service() { + unset http_proxy + docker run -d --name="test-comps-text2image" -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e MODEL=stabilityai/stable-diffusion-xl-base-1.0 -p 9379:9379 --ipc=host opea/text2image:latest + sleep 30s +} + +function validate_microservice() { + result=$(http_proxy="" curl http://localhost:9379/v1/text2image -XPOST -d '{"prompt":"An astronaut riding a green horse", "num_images_per_prompt":1}' -H 'Content-Type: application/json') + if [[ $result == *"images"* ]]; then + echo "Result correct." + else + echo "Result wrong." + docker logs test-comps-text2image + exit 1 + fi + +} + +function stop_docker() { + cid=$(docker ps -aq --filter "name=test-comps-text2image*") + if [[ ! -z "$cid" ]]; then docker stop $cid && docker rm $cid && sleep 1s; fi +} + +function main() { + + stop_docker + + build_docker_images + start_service + + validate_microservice + + stop_docker + echo y | docker system prune + +} + +main From 7fb8c6b9afcc671c76eddac5116e226902144914 Mon Sep 17 00:00:00 2001 From: ZePan110 Date: Tue, 8 Oct 2024 20:57:05 +0800 Subject: [PATCH 09/49] Compatible with different platforms. (#766) * Compatible with different platforms. Signed-off-by: ZePan110 * Fix issue. Signed-off-by: ZePan110 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix issue Signed-off-by: ZePan110 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: ZePan110 Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Chun Tao --- .github/workflows/_run-docker-compose.yml | 27 +++++++++++++++++------ 1 file changed, 20 insertions(+), 7 deletions(-) diff --git a/.github/workflows/_run-docker-compose.yml b/.github/workflows/_run-docker-compose.yml index 5b89f67b28..c1b6312d09 100644 --- a/.github/workflows/_run-docker-compose.yml +++ b/.github/workflows/_run-docker-compose.yml @@ -24,7 +24,7 @@ jobs: get-test-case: runs-on: ubuntu-latest outputs: - test_cases: ${{ steps.test-case-matrix.outputs.test_cases }} + run_matrix: ${{ steps.test-case-matrix.outputs.run_matrix }} CHECKOUT_REF: ${{ steps.get-checkout-ref.outputs.CHECKOUT_REF }} steps: - name: Get checkout ref @@ -49,19 +49,31 @@ jobs: id: test-case-matrix run: | set -x + run_matrix="{\"include\":[" service_l=$(echo ${{ inputs.service }} | tr '[:upper:]' '[:lower:]') cd ${{ github.workspace }}/tests - test_cases=$(find . -type f -name "test_${service_l}*.sh" -print | jq -R '.' | jq -sc '.') - echo "test_cases=$test_cases" >> $GITHUB_OUTPUT - # TODO: get hardware for test matrix + # test_cases=$(find . -type f -name "test_${service_l}*.sh" -print | jq -R '.' | jq -sc '.') + test_cases=$(find . -type f -name "test_${service_l}*.sh") + for script in $test_cases; do + echo $script + if echo "$script" | grep -q "on"; then + hardware=$(echo $script | cut -d'/' -f3 | cut -d'.' -f1 | awk -F'on_' '{print $2}') + run_matrix="${run_matrix}{\"test_case\":\"${script}\",\"hardware\":\"${hardware}\"}," + else + run_matrix="${run_matrix}{\"test_case\":\"${script}\",\"hardware\":\"intel_cpu\"}," + fi + done + run_matrix=$(echo "$run_matrix" | sed 's/,$//') + run_matrix=$run_matrix"]}" + echo "run_matrix=${run_matrix}" + echo "run_matrix=${run_matrix}" >> $GITHUB_OUTPUT run-test: needs: [get-test-case] strategy: matrix: - test_case: ${{ fromJSON(needs.get-test-case.outputs.test_cases) }} - fail-fast: false - runs-on: gaudi + include: ${{ fromJSON(needs.get-test-case.outputs.run_matrix).include }} + runs-on: ${{ matrix.hardware }} continue-on-error: true steps: - name: Clean up Working Directory @@ -86,6 +98,7 @@ jobs: PINECONE_KEY: ${{ secrets.PINECONE_KEY }} service: ${{ inputs.service }} test_case: ${{ matrix.test_case }} + hardware: ${{ matrix.hardware }} run: | cd ${{ github.workspace }}/tests service=$(echo "${test_case}" | cut -d'_' -f2- |cut -d'.' -f1) From 817c0f1e6c90496dae7e9298e3f7df65ad0723b6 Mon Sep 17 00:00:00 2001 From: ZePan110 Date: Wed, 9 Oct 2024 10:54:16 +0800 Subject: [PATCH 10/49] Optimize path and link validity check. (#745) Signed-off-by: ZePan110 Signed-off-by: Chun Tao --- .../workflows/pr-dockerfile-path-scan.yaml | 99 +++++++++++-------- 1 file changed, 58 insertions(+), 41 deletions(-) diff --git a/.github/workflows/pr-dockerfile-path-scan.yaml b/.github/workflows/pr-dockerfile-path-scan.yaml index fddd0a4dff..d6f23a6f3b 100644 --- a/.github/workflows/pr-dockerfile-path-scan.yaml +++ b/.github/workflows/pr-dockerfile-path-scan.yaml @@ -165,36 +165,47 @@ jobs: - name: Checkout Repo GenAIComps uses: actions/checkout@v4 + with: + fetch-depth: 0 - name: Check the Validity of Hyperlinks # ignore_links=("https://platform.openai.com/docs/api-reference/fine-tuning" - # "https://platform.openai.com/docs/api-reference/" - # "https://openai.com/index/whisper/" - # "https://platform.openai.com/docs/api-reference/chat/create") + # "https://platform.openai.com/docs/api-reference/" + # "https://openai.com/index/whisper/" + # "https://platform.openai.com/docs/api-reference/chat/create") run: | cd ${{github.workspace}} fail="FALSE" - url_lines=$(grep -Eo '\]\(http[s]?://[^)]+\)' --include='*.md' -r .) - if [ -n "$url_lines" ]; then - for url_line in $url_lines; do - url=$(echo "$url_line"|cut -d '(' -f2 | cut -d ')' -f1|sed 's/\.git$//') - path=$(echo "$url_line"|cut -d':' -f1 | cut -d'/' -f2-) - if [[ "https://platform.openai.com/docs/api-reference/fine-tuning" == "$url" || "https://platform.openai.com/docs/api-reference/" == "$url" || "https://openai.com/index/whisper/" == "$url" || "https://platform.openai.com/docs/api-reference/chat/create" == "$url" ]]; then - echo "Link "$url" from ${{github.workspace}}/$path need to be verified by a real person." - else - response=$(curl -L -s -o /dev/null -w "%{http_code}" "$url") - if [ "$response" -ne 200 ]; then - echo "**********Validation failed, try again**********" - response_retry=$(curl -s -o /dev/null -w "%{http_code}" "$url") - if [ "$response_retry" -eq 200 ]; then - echo "*****Retry successfully*****" + merged_commit=$(git log -1 --format='%H') + changed_files="$(git diff --name-status --diff-filter=ARM ${{ github.event.pull_request.base.sha }} ${merged_commit} | awk '/\.md$/ {print $NF}')" + if [ -n "changed_files" ]; then + for changed_file in $changed_files; do + echo $changed_file + url_lines=$(grep -H -Eo '\]\(http[s]?://[^)]+\)' "$changed_file" | grep -Ev 'GenAIComps/blob/main') + if [ -n "$url_lines" ]; then + for url_line in $url_lines; do + url=$(echo "$url_line"|cut -d '(' -f2 | cut -d ')' -f1|sed 's/\.git$//') + path=$(echo "$url_line"|cut -d':' -f1 | cut -d'/' -f2-) + if [[ "https://platform.openai.com/docs/api-reference/fine-tuning" == "$url" || "https://platform.openai.com/docs/api-reference/" == "$url" || "https://openai.com/index/whisper/" == "$url" || "https://platform.openai.com/docs/api-reference/chat/create" == "$url" ]]; then + echo "Link "$url" from ${{github.workspace}}/$path need to be verified by a real person." else - echo "Invalid link from ${{github.workspace}}/$path: $url" - fail="TRUE" + response=$(curl -L -s -o /dev/null -w "%{http_code}" "$url") + if [ "$response" -ne 200 ]; then + echo "**********Validation failed, try again**********" + response_retry=$(curl -s -o /dev/null -w "%{http_code}" "$url") + if [ "$response_retry" -eq 200 ]; then + echo "*****Retry successfully*****" + else + echo "Invalid link from ${{github.workspace}}/$path: $url" + fail="TRUE" + fi + fi fi - fi + done fi done + else + echo "No changed .md file." fi if [[ "$fail" == "TRUE" ]]; then @@ -212,6 +223,8 @@ jobs: - name: Checkout Repo GenAIComps uses: actions/checkout@v4 + with: + fetch-depth: 0 - name: Checking Relative Path Validity run: | @@ -225,33 +238,34 @@ jobs: branch="https://github.com/opea-project/GenAIComps/blob/${{ github.event.pull_request.head.ref }}" fi link_head="https://github.com/opea-project/GenAIComps/blob/main" + + merged_commit=$(git log -1 --format='%H') + changed_files="$(git diff --name-status --diff-filter=ARM ${{ github.event.pull_request.base.sha }} ${merged_commit} | awk '/\.md$/ {print $NF}')" png_lines=$(grep -Eo '\]\([^)]+\)' --include='*.md' -r .|grep -Ev 'http') if [ -n "$png_lines" ]; then for png_line in $png_lines; do refer_path=$(echo "$png_line"|cut -d':' -f1 | cut -d'/' -f2-) png_path=$(echo "$png_line"|cut -d '(' -f2 | cut -d ')' -f1) + if [[ "${png_path:0:1}" == "/" ]]; then - check_path=${{github.workspace}}$png_path - elif [[ "${png_path:0:1}" == "#" ]]; then - check_path=${{github.workspace}}/$refer_path$png_path + check_path=$png_path + elif [[ "$png_path" == *#* ]]; then + relative_path=$(echo "$png_path" | cut -d '#' -f1) + if [ -n "$relative_path" ]; then + check_path=$(dirname "$refer_path")/$relative_path + png_path=$(echo "$png_path" | awk -F'#' '{print "#" $2}') + else + check_path=$refer_path + fi else - check_path=${{github.workspace}}/$(dirname "$refer_path")/$png_path + check_path=$(dirname "$refer_path")/$png_path fi - real_path=$(realpath $check_path) - if [ $? -ne 0 ]; then - echo "Path $png_path in file ${{github.workspace}}/$refer_path does not exist" - fail="TRUE" - else - url=$link_head$(echo "$real_path" | sed 's|.*/GenAIComps||') - response=$(curl -I -L -s -o /dev/null -w "%{http_code}" "$url") - if [ "$response" -ne 200 ]; then - echo "**********Validation failed, try again**********" - response_retry=$(curl -s -o /dev/null -w "%{http_code}" "$url") - if [ "$response_retry" -eq 200 ]; then - echo "*****Retry successfully*****" - else - echo "Retry failed. Check branch ${{ github.event.pull_request.head.ref }}" - url_dev=$branch$(echo "$real_path" | sed 's|.*/GenAIComps||') + + if [ -e "$check_path" ]; then + real_path=$(realpath $check_path) + if [[ "$png_line" == *#* ]]; then + if [ -n "changed_files" ] && echo "$changed_files" | grep -q "^${refer_path}$"; then + url_dev=$branch$(echo "$real_path" | sed 's|.*/GenAIComps||')$png_path response=$(curl -I -L -s -o /dev/null -w "%{http_code}" "$url_dev") if [ "$response" -ne 200 ]; then echo "**********Validation failed, try again**********" @@ -259,14 +273,17 @@ jobs: if [ "$response_retry" -eq 200 ]; then echo "*****Retry successfully*****" else - echo "Invalid path from ${{github.workspace}}/$refer_path: $png_path" + echo "Invalid path from ${{github.workspace}}/$refer_path: $png_path, link: $url_dev" fail="TRUE" fi else - echo "Check branch ${{ github.event.pull_request.head.ref }} successfully." + echo "Validation succeed $png_line" fi fi fi + else + echo "$check_path does not exist" + fail="TRUE" fi done fi From a14c76f7a528be413c0e61eed7d3d3ade491adbf Mon Sep 17 00:00:00 2001 From: "chen, suyue" Date: Wed, 9 Oct 2024 16:09:53 +0800 Subject: [PATCH 11/49] Add timeout for ut test (#773) Signed-off-by: chensuyue Signed-off-by: Chun Tao --- .github/workflows/mix-megaservice-test.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/mix-megaservice-test.yml b/.github/workflows/mix-megaservice-test.yml index 3837a240b9..82e7058110 100644 --- a/.github/workflows/mix-megaservice-test.yml +++ b/.github/workflows/mix-megaservice-test.yml @@ -32,6 +32,7 @@ env: jobs: MegaService: runs-on: gaudi + timeout-minutes: 10 steps: - name: Clean Up Working Directory run: | From 8fae4827bea414564375ec2a20116b6c30f8595c Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 9 Oct 2024 22:28:00 +0000 Subject: [PATCH 12/49] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Signed-off-by: Chun Tao --- comps/animation/Dockerfile | 3 +++ 1 file changed, 3 insertions(+) diff --git a/comps/animation/Dockerfile b/comps/animation/Dockerfile index 97299a71b5..358c09b03e 100644 --- a/comps/animation/Dockerfile +++ b/comps/animation/Dockerfile @@ -1,3 +1,6 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + # Use a base image FROM python:3.11-slim From 1879a1d2cee50672400d95b97dffbf932efe0f0e Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Thu, 10 Oct 2024 02:51:33 +0000 Subject: [PATCH 13/49] test hyperlink Signed-off-by: Chun Tao --- comps/animation/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comps/animation/README.md b/comps/animation/README.md index d9ef21d2bf..757909c5e8 100644 --- a/comps/animation/README.md +++ b/comps/animation/README.md @@ -60,7 +60,7 @@ Once microservice starts, user can use below script to validate the running micr ```bash cd GenAIComps export ip_address=$(hostname -I | awk '{print $1}') -curl http://${ip_address}:7860/v1/animation -X POST -H "Content-Type: application/json" -d @comps/animation/assets/audio/sample_question.json +# curl http://${ip_address}:7860/v1/animation -X POST -H "Content-Type: application/json" -d @comps/animation/assets/audio/sample_question.json ``` or @@ -75,7 +75,7 @@ The expected output is a message similar to the following: ```bash "Status code: 200" "Check $OUTFILE for the result." -"{'id': '33dd8249228b0e011a33b449af9aa776', 'video_save_path': '/home/demo/ctao/forks/GenAIComps/comps/animation/outputs/result.mp4'}" +"{'id': '33dd8249228b0e011a33b449af9aa776', 'video_save_path': '.../GenAIComps/comps/animation/outputs/result.mp4'}" ``` Please find "./outputs/result.mp4" as a reference generated video. From cc938fbecb284728dc50d65b2a99281aaf5ea5d8 Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Thu, 10 Oct 2024 02:53:00 +0000 Subject: [PATCH 14/49] test hyperlink Signed-off-by: Chun Tao --- comps/animation/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comps/animation/README.md b/comps/animation/README.md index 757909c5e8..25579b8ee0 100644 --- a/comps/animation/README.md +++ b/comps/animation/README.md @@ -7,7 +7,7 @@ The avatar animation model is a combination of two models: Wav2Lip and GAN-based ## 1.1 Build the Docker image ```bash -git clone https://github.com/opea-project/GenAIComps.git +# git clone https://github.com/opea-project/GenAIComps.git cd GenAIComps docker build -t opea/animation:latest -f comps/animation/Dockerfile_hpu . ``` From 697fdbdb1f7ca5841e9d6433e3443996c93318fb Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Thu, 10 Oct 2024 02:55:05 +0000 Subject: [PATCH 15/49] test hyperlink issue Signed-off-by: Chun Tao --- comps/animation/README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/comps/animation/README.md b/comps/animation/README.md index 25579b8ee0..e8502d0fdc 100644 --- a/comps/animation/README.md +++ b/comps/animation/README.md @@ -7,7 +7,7 @@ The avatar animation model is a combination of two models: Wav2Lip and GAN-based ## 1.1 Build the Docker image ```bash -# git clone https://github.com/opea-project/GenAIComps.git +git clone https://github.com/opea-project/GenAIComps.git cd GenAIComps docker build -t opea/animation:latest -f comps/animation/Dockerfile_hpu . ``` @@ -60,7 +60,6 @@ Once microservice starts, user can use below script to validate the running micr ```bash cd GenAIComps export ip_address=$(hostname -I | awk '{print $1}') -# curl http://${ip_address}:7860/v1/animation -X POST -H "Content-Type: application/json" -d @comps/animation/assets/audio/sample_question.json ``` or From 120edae77648808512fcc6804567b9d52378dca0 Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Thu, 10 Oct 2024 02:56:18 +0000 Subject: [PATCH 16/49] test hyperlink issue Signed-off-by: Chun Tao --- comps/animation/README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/comps/animation/README.md b/comps/animation/README.md index e8502d0fdc..495c700dd4 100644 --- a/comps/animation/README.md +++ b/comps/animation/README.md @@ -7,7 +7,6 @@ The avatar animation model is a combination of two models: Wav2Lip and GAN-based ## 1.1 Build the Docker image ```bash -git clone https://github.com/opea-project/GenAIComps.git cd GenAIComps docker build -t opea/animation:latest -f comps/animation/Dockerfile_hpu . ``` From 4bfd1f363ff8e719064168e726351a316cb8175f Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Thu, 10 Oct 2024 16:05:25 +0000 Subject: [PATCH 17/49] put back hyperlinks in readme Signed-off-by: Chun Tao --- comps/animation/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/comps/animation/README.md b/comps/animation/README.md index 495c700dd4..3f566a9ec6 100644 --- a/comps/animation/README.md +++ b/comps/animation/README.md @@ -7,6 +7,7 @@ The avatar animation model is a combination of two models: Wav2Lip and GAN-based ## 1.1 Build the Docker image ```bash +git clone https://github.com/opea-project/GenAIComps.git cd GenAIComps docker build -t opea/animation:latest -f comps/animation/Dockerfile_hpu . ``` @@ -59,6 +60,7 @@ Once microservice starts, user can use below script to validate the running micr ```bash cd GenAIComps export ip_address=$(hostname -I | awk '{print $1}') +curl http://${ip_address}:7860/v1/animation -X POST -H "Content-Type: application/json" -d @comps/animation/assets/audio/sample_question.json ``` or From 10b3b3400c9536b71232a53b7b9618052835a088 Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Thu, 10 Oct 2024 16:16:45 +0000 Subject: [PATCH 18/49] remove possible error hyperlink Signed-off-by: Chun Tao --- comps/animation/README.md | 8 -------- 1 file changed, 8 deletions(-) diff --git a/comps/animation/README.md b/comps/animation/README.md index 3f566a9ec6..1aad4e2b2d 100644 --- a/comps/animation/README.md +++ b/comps/animation/README.md @@ -57,14 +57,6 @@ python3 animation.py --inference_mode $INFERENCE_MODE --checkpoint_path $CHECKPO Once microservice starts, user can use below script to validate the running microservice. -```bash -cd GenAIComps -export ip_address=$(hostname -I | awk '{print $1}') -curl http://${ip_address}:7860/v1/animation -X POST -H "Content-Type: application/json" -d @comps/animation/assets/audio/sample_question.json -``` - -or - ```bash cd GenAIComps/comps/animation python3 test_animation_server.py From 09ab93486c6452a49f533ee304117c53eeefd2ab Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Thu, 10 Oct 2024 16:19:24 +0000 Subject: [PATCH 19/49] put hyperlink back Signed-off-by: Chun Tao --- comps/animation/README.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/comps/animation/README.md b/comps/animation/README.md index 1aad4e2b2d..3f566a9ec6 100644 --- a/comps/animation/README.md +++ b/comps/animation/README.md @@ -57,6 +57,14 @@ python3 animation.py --inference_mode $INFERENCE_MODE --checkpoint_path $CHECKPO Once microservice starts, user can use below script to validate the running microservice. +```bash +cd GenAIComps +export ip_address=$(hostname -I | awk '{print $1}') +curl http://${ip_address}:7860/v1/animation -X POST -H "Content-Type: application/json" -d @comps/animation/assets/audio/sample_question.json +``` + +or + ```bash cd GenAIComps/comps/animation python3 test_animation_server.py From 303eb1f01c01528ad5e359ef79c3a4041748ce0a Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Tue, 15 Oct 2024 22:34:59 +0000 Subject: [PATCH 20/49] major update to use FastAPI for wav2lip, and structure component format Signed-off-by: Chun Tao --- comps/animation/README.md | 81 --------- comps/animation/outputs/result.mp4 | Bin 227121 -> 0 bytes comps/animation/wav2lip/Dockerfile | 24 +++ comps/animation/wav2lip/README.md | 106 ++++++++++++ comps/animation/wav2lip/animation.py | 59 +++++++ .../assets/audio/sample_question.json | 0 .../{ => wav2lip}/assets/avatar1.jpg | Bin .../{ => wav2lip}/assets/avatar2.jpg | Bin .../{ => wav2lip}/assets/avatar3.png | Bin .../{ => wav2lip}/assets/avatar4.png | Bin .../{ => wav2lip}/assets/avatar5.png | Bin .../{ => wav2lip}/assets/avatar6.png | Bin .../animation/{ => wav2lip}/assets/design.png | Bin .../{ => wav2lip}/assets/eg3_ref.wav | Bin .../{ => wav2lip}/assets/flowchart.png | Bin .../animation/{ => wav2lip}/assets/gaudi.png | Bin .../{ => wav2lip}/assets/opea_gh_qr.png | Bin .../{ => wav2lip}/assets/opea_qr.png | Bin .../wav2lip/assets/outputs/result.mp4 | Bin 0 -> 225848 bytes comps/animation/{ => wav2lip}/assets/xeon.jpg | Bin .../check_animation_server.py} | 2 +- .../{ => wav2lip/dependency}/Dockerfile | 13 +- .../dependency/Dockerfile.intel_hpu} | 15 +- .../animation/wav2lip/dependency/__init__.py | 2 + .../dependency/check_wav2lip_server.py | 19 +++ .../{ => wav2lip/dependency}/entrypoint.sh | 4 +- .../{ => wav2lip/dependency}/utils.py | 28 ++-- .../dependency/wav2lip_server.py} | 155 +++++++----------- comps/animation/{ => wav2lip}/docker_run.sh | 0 .../animation/{ => wav2lip}/requirements.txt | 3 +- comps/cores/mega/constants.py | 1 + 31 files changed, 295 insertions(+), 217 deletions(-) delete mode 100644 comps/animation/README.md delete mode 100644 comps/animation/outputs/result.mp4 create mode 100644 comps/animation/wav2lip/Dockerfile create mode 100644 comps/animation/wav2lip/README.md create mode 100644 comps/animation/wav2lip/animation.py rename comps/animation/{ => wav2lip}/assets/audio/sample_question.json (100%) rename comps/animation/{ => wav2lip}/assets/avatar1.jpg (100%) rename comps/animation/{ => wav2lip}/assets/avatar2.jpg (100%) rename comps/animation/{ => wav2lip}/assets/avatar3.png (100%) rename comps/animation/{ => wav2lip}/assets/avatar4.png (100%) rename comps/animation/{ => wav2lip}/assets/avatar5.png (100%) rename comps/animation/{ => wav2lip}/assets/avatar6.png (100%) rename comps/animation/{ => wav2lip}/assets/design.png (100%) rename comps/animation/{ => wav2lip}/assets/eg3_ref.wav (100%) rename comps/animation/{ => wav2lip}/assets/flowchart.png (100%) rename comps/animation/{ => wav2lip}/assets/gaudi.png (100%) rename comps/animation/{ => wav2lip}/assets/opea_gh_qr.png (100%) rename comps/animation/{ => wav2lip}/assets/opea_qr.png (100%) create mode 100644 comps/animation/wav2lip/assets/outputs/result.mp4 rename comps/animation/{ => wav2lip}/assets/xeon.jpg (100%) rename comps/animation/{test_animation_server.py => wav2lip/check_animation_server.py} (91%) rename comps/animation/{ => wav2lip/dependency}/Dockerfile (81%) rename comps/animation/{Dockerfile_hpu => wav2lip/dependency/Dockerfile.intel_hpu} (81%) create mode 100644 comps/animation/wav2lip/dependency/__init__.py create mode 100644 comps/animation/wav2lip/dependency/check_wav2lip_server.py rename comps/animation/{ => wav2lip/dependency}/entrypoint.sh (87%) rename comps/animation/{ => wav2lip/dependency}/utils.py (95%) rename comps/animation/{animation.py => wav2lip/dependency/wav2lip_server.py} (61%) rename comps/animation/{ => wav2lip}/docker_run.sh (100%) rename comps/animation/{ => wav2lip}/requirements.txt (95%) diff --git a/comps/animation/README.md b/comps/animation/README.md deleted file mode 100644 index 3f566a9ec6..0000000000 --- a/comps/animation/README.md +++ /dev/null @@ -1,81 +0,0 @@ -# Avatar Animation Microservice - -The avatar animation model is a combination of two models: Wav2Lip and GAN-based face generator (GFPGAN). The Wav2Lip model is used to generate lip movements from an audio file, and the GFPGAN model is used to generate a high-quality face image from a low-quality face image. The avatar animation microservices takes an audio piece and a low-quality face image/video as input, fuses mel-spectrogram from the audio with frame(s) from the image/video, and generates a high-quality video of the face image with lip movements synchronized with the audio. - -# 🚀1. Start Microservice with Docker (option 1) - -## 1.1 Build the Docker image - -```bash -git clone https://github.com/opea-project/GenAIComps.git -cd GenAIComps -docker build -t opea/animation:latest -f comps/animation/Dockerfile_hpu . -``` - -## 1.2. Set environment variables - -```bash -export ip_address=$(hostname -I | awk '{print $1}') -export ANIMATION_PORT=7860 -export INFERENCE_MODE='wav2clip+gfpgan' -export CHECKPOINT_PATH='/usr/local/lib/python3.10/dist-packages/Wav2Lip/checkpoints/wav2lip_gan.pth' -export FACE='assets/avatar1.jpg' -# export AUDIO='assets/eg3_ref.wav' # audio file path is optional, will use base64str as input if is 'None' -export AUDIO='None' -export FACESIZE=96 -export OUTFILE="$(pwd)/comps/animation/outputs/result.mp4" -export GFPGAN_MODEL_VERSION=1.3 -export UPSCALE_FACTOR=1 -export FPS=10 -``` - -## 1.3. Run the Docker container - - - -```bash -docker run --privileged -d --runtime=habana --cap-add=sys_nice --net=host --ipc=host --name "animation-service" -v /var/run/docker.sock:/var/run/docker.sock -v /usr/bin/docker:/usr/bin/docker -v $(pwd):$(pwd) -w /home/user/comps/animation -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e PYTHON=/usr/bin/python3.10 -e INFERENCE_MODE=$INFERENCE_MODE -e CHECKPOINT_PATH=$CHECKPOINT_PATH -e FACE=$FACE -e AUDIO=$AUDIO -e FACESIZE=$FACESIZE -e OUTFILE=$OUTFILE -e GFPGAN_MODEL_VERSION=$GFPGAN_MODEL_VERSION -e UPSCALE_FACTOR=$UPSCALE_FACTOR -e FPS=$FPS -e ANIMATION_PORT=$ANIMATION_PORT opea/animation:latest -``` - -# 🚀2. Start Microservice with Python (option 2) - -Follow 1.1 and 1.2 steps from the above section to build the Docker image and set the environment variables. - -## 2.1. Run the Docker container by overriding the entrypoint - -```bash -docker run --privileged --rm -it --runtime=habana --cap-add=sys_nice --net=host --ipc=host --name "animation-service" -v /var/run/docker.sock:/var/run/docker.sock -v /usr/bin/docker:/usr/bin/docker -v $(pwd):$(pwd) -w /home/user/comps/animation -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e PYTHON=/usr/bin/python3.10 -e INFERENCE_MODE=$INFERENCE_MODE -e CHECKPOINT_PATH=$CHECKPOINT_PATH -e FACE=$FACE -e AUDIO=$AUDIO -e FACESIZE=$FACESIZE -e OUTFILE=$OUTFILE -e GFPGAN_MODEL_VERSION=$GFPGAN_MODEL_VERSION -e UPSCALE_FACTOR=$UPSCALE_FACTOR -e FPS=$FPS -e ANIMATION_PORT=$ANIMATION_PORT --entrypoint /bin/bash opea/animation:latest -``` - -## 2.2 Inside the container, run the following command to start the microservice - -```bash -python3 animation.py --inference_mode $INFERENCE_MODE --checkpoint_path $CHECKPOINT_PATH --face $FACE --audio $AUDIO --outfile $OUTFILE --img_size $FACESIZE -v $GFPGAN_MODEL_VERSION -s $UPSCALE_FACTOR --fps $FPS --only_center_face --bg_upsampler None -``` - -# 🚀3. Validate Microservice - -Once microservice starts, user can use below script to validate the running microservice. - -```bash -cd GenAIComps -export ip_address=$(hostname -I | awk '{print $1}') -curl http://${ip_address}:7860/v1/animation -X POST -H "Content-Type: application/json" -d @comps/animation/assets/audio/sample_question.json -``` - -or - -```bash -cd GenAIComps/comps/animation -python3 test_animation_server.py -``` - -The expected output is a message similar to the following: - -```bash -"Status code: 200" -"Check $OUTFILE for the result." -"{'id': '33dd8249228b0e011a33b449af9aa776', 'video_save_path': '.../GenAIComps/comps/animation/outputs/result.mp4'}" -``` - -Please find "./outputs/result.mp4" as a reference generated video. diff --git a/comps/animation/outputs/result.mp4 b/comps/animation/outputs/result.mp4 deleted file mode 100644 index 1cdba400dfc066db506032c186d83d154f884556..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 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+ +RUN useradd -m -s /bin/bash user && \ + mkdir -p /home/user && \ + chown -R user /home/user/ +USER user + +ENV LANG=C.UTF-8 +ARG ARCH=cpu + +COPY comps /home/user/comps + +RUN pip install --no-cache-dir --upgrade pip && \ + pip install --no-cache-dir -r /home/user/comps/animation/requirements.txt ; + +ENV PYTHONPATH=$PYTHONPATH:/home/user + +WORKDIR /home/user/comps/animation + +ENTRYPOINT ["python", "animation.py"] \ No newline at end of file diff --git a/comps/animation/wav2lip/README.md b/comps/animation/wav2lip/README.md new file mode 100644 index 0000000000..1c022a863b --- /dev/null +++ b/comps/animation/wav2lip/README.md @@ -0,0 +1,106 @@ +# Avatar Animation Microservice + +The avatar animation model is a combination of two models: Wav2Lip and GAN-based face generator (GFPGAN). The Wav2Lip model is used to generate lip movements from an audio file, and the GFPGAN model is used to generate a high-quality face image from a low-quality face image. The avatar animation microservices takes an audio piece and a low-quality face image/video as input, fuses mel-spectrogram from the audio with frame(s) from the image/video, and generates a high-quality video of the face image with lip movements synchronized with the audio. + +# 🚀1. Start Microservice with Docker (option 1) + +## 1.1 Build the Docker images + +### 1.1.1 Wav2Lip Server image + +```bash +git clone https://github.com/opea-project/GenAIComps.git +cd GenAIComps +``` + +- Xeon CPU + +```bash +docker build -t opea/wav2lip:latest -f comps/animation/dependency/Dockerfile . +``` + +- Gaudi2 HPU + +```bash +docker build -t opea/wav2lip-gaudi:latest -f comps/animation/dependency/Dockerfile.intel_hpu . +``` + +### 1.1.2 Animation server image +```bash +docker build -t opea/animation:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/animation/wav2lip/Dockerfile . +``` + +## 1.2. Set environment variables + +```bash +export ip_address=$(hostname -I | awk '{print $1}') +# export DEVICE="cpu" +export DEVICE="hpu" +export WAV2LIP_PORT=7860 +export ANIMATION_PORT=9066 +export INFERENCE_MODE='wav2lip+gfpgan' +export CHECKPOINT_PATH='/usr/local/lib/python3.10/dist-packages/Wav2Lip/checkpoints/wav2lip_gan.pth' +export FACE='assets/avatar1.jpg' +# export AUDIO='assets/eg3_ref.wav' # audio file path is optional, will use base64str in the post request as input if is 'None' +export AUDIO='None' +export FACESIZE=96 +export OUTFILE="$(pwd)/comps/animation/wav2lip/assets/outputs/result.mp4" +export GFPGAN_MODEL_VERSION=1.3 +export UPSCALE_FACTOR=1 +export FPS=10 +``` + +# 🚀2. Run the Docker container +## 2.1 Run Wav2Lip Microservice +- Xeon CPU + +```bash +docker run --privileged -d -p 7860:7860 --ipc=host --name "wav2lip-service" -v /var/run/docker.sock:/var/run/docker.sock -v /usr/bin/docker:/usr/bin/docker -v $(pwd):$(pwd) -w /home/user/comps/animation -e PYTHON=/usr/bin/python3.11 -e DEVICE=$DEVICE -e INFERENCE_MODE=$INFERENCE_MODE -e CHECKPOINT_PATH=$CHECKPOINT_PATH -e FACE=$FACE -e AUDIO=$AUDIO -e FACESIZE=$FACESIZE -e OUTFILE=$OUTFILE -e GFPGAN_MODEL_VERSION=$GFPGAN_MODEL_VERSION -e UPSCALE_FACTOR=$UPSCALE_FACTOR -e FPS=$FPS -e WAV2LIP_PORT=$WAV2LIP_PORT opea/wav2lip:latest +``` + +- Gaudi2 HPU + +```bash +docker run --privileged -d --runtime=habana --cap-add=sys_nice --net=host --ipc=host --name "wav2lip-gaudi-service" -v /var/run/docker.sock:/var/run/docker.sock -v /usr/bin/docker:/usr/bin/docker -v $(pwd):$(pwd) -w /home/user/comps/animation -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e PYTHON=/usr/bin/python3.10 -e DEVICE=$DEVICE -e INFERENCE_MODE=$INFERENCE_MODE -e CHECKPOINT_PATH=$CHECKPOINT_PATH -e FACE=$FACE -e AUDIO=$AUDIO -e FACESIZE=$FACESIZE -e OUTFILE=$OUTFILE -e GFPGAN_MODEL_VERSION=$GFPGAN_MODEL_VERSION -e UPSCALE_FACTOR=$UPSCALE_FACTOR -e FPS=$FPS -e WAV2LIP_PORT=$WAV2LIP_PORT opea/wav2lip-gaudi:latest +``` + +## 2.2 Run Animation Microservice +```bash +docker run -d -p 9066:9066 --ipc=host --name "animation-service" -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e WAV2LIP_ENDPOINT=http://$ip_address:7860 opea/animation:latest +``` + +# 🚀3. Validate Microservice + +Once microservice starts, user can use below script to validate the running microservice. + +## 3.1 Validate Wav2Lip service + +```bash +cd GenAIComps/comps/animation/wav2lip +python3 dependency/check_wav2lip_server.py +``` + +## 3.2 Validate Animation service + +```bash +cd GenAIComps +export ip_address=$(hostname -I | awk '{print $1}') +curl http://${ip_address}:7860/v1/animation -X POST -H "Content-Type: application/json" -d @comps/animation/wav2lip/assets/audio/sample_question.json +``` + +or + +```bash +cd GenAIComps/comps/animation/wav2lip +python3 check_animation_server.py +``` + +The expected output is a message similar to the following: + +```bash +"Status code: 200" +"Check $OUTFILE for the result." +"{'id': '33dd8249228b0e011a33b449af9aa776', 'video_save_path': '.../GenAIComps/comps/animation/wav2lip/assets/outputs/result.mp4'}" +``` + +Please find "./outputs/result.mp4" as a reference generated video. diff --git a/comps/animation/wav2lip/animation.py b/comps/animation/wav2lip/animation.py new file mode 100644 index 0000000000..05ee97f271 --- /dev/null +++ b/comps/animation/wav2lip/animation.py @@ -0,0 +1,59 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 +# Copyright (c) 2013--2023, librosa development team. +# Copyright 1999-2003 The OpenLDAP Foundation, Redwood City, California, USA. All Rights Reserved. +# Copyright (c) 2012, Anaconda, Inc. All rights reserved. + +import os +import time +import json +import requests + +# GenAIComps +from comps import CustomLogger +logger = CustomLogger("animation") +logflag = os.getenv("LOGFLAG", False) +from comps import ( + Base64ByteStrDoc, + VideoPath, + ServiceType, + opea_microservices, + register_microservice, + register_statistics, + statistics_dict, +) + +# Register the microservice +@register_microservice( + name="opea_service@animation", + service_type=ServiceType.ANIMATION, + endpoint="/v1/animation", + host="0.0.0.0", + port=9066, + input_datatype=Base64ByteStrDoc, + output_datatype=VideoPath, +) +@register_statistics(names=["opea_service@animation"]) +async def animate(audio: Base64ByteStrDoc): + start = time.time() + + byte_str = audio.byte_str + inputs = {"audio": byte_str} + if logflag: + logger.info(inputs) + + response = requests.post(url=f"{wav2lip_endpoint}/v1/wav2lip", data=json.dumps(inputs), proxies={"http": None}) + + outfile = response.json()["wav2lip_result"] + if logflag: + logger.info(response) + logger.info(f"Video generated successfully, check {outfile} for the result.") + + statistics_dict["opea_service@animation"].append_latency(time.time() - start, None) + return VideoPath(video_path=outfile) + + +if __name__ == "__main__": + wav2lip_endpoint = os.getenv("WAV2LIP_ENDPOINT", "http://localhost:7860") + logger.info("[animation - router] Animation initialized.") + opea_microservices["opea_service@animation"].start() diff --git a/comps/animation/assets/audio/sample_question.json b/comps/animation/wav2lip/assets/audio/sample_question.json similarity index 100% rename from comps/animation/assets/audio/sample_question.json rename to comps/animation/wav2lip/assets/audio/sample_question.json diff --git a/comps/animation/assets/avatar1.jpg b/comps/animation/wav2lip/assets/avatar1.jpg similarity index 100% rename from comps/animation/assets/avatar1.jpg rename to comps/animation/wav2lip/assets/avatar1.jpg diff --git a/comps/animation/assets/avatar2.jpg b/comps/animation/wav2lip/assets/avatar2.jpg similarity index 100% rename from comps/animation/assets/avatar2.jpg rename to comps/animation/wav2lip/assets/avatar2.jpg diff --git a/comps/animation/assets/avatar3.png b/comps/animation/wav2lip/assets/avatar3.png similarity index 100% rename from comps/animation/assets/avatar3.png rename to comps/animation/wav2lip/assets/avatar3.png diff --git a/comps/animation/assets/avatar4.png b/comps/animation/wav2lip/assets/avatar4.png similarity index 100% rename from comps/animation/assets/avatar4.png rename to comps/animation/wav2lip/assets/avatar4.png diff --git a/comps/animation/assets/avatar5.png b/comps/animation/wav2lip/assets/avatar5.png similarity index 100% rename from comps/animation/assets/avatar5.png rename to comps/animation/wav2lip/assets/avatar5.png diff --git a/comps/animation/assets/avatar6.png b/comps/animation/wav2lip/assets/avatar6.png similarity index 100% rename from comps/animation/assets/avatar6.png rename to comps/animation/wav2lip/assets/avatar6.png diff --git a/comps/animation/assets/design.png b/comps/animation/wav2lip/assets/design.png similarity index 100% rename from comps/animation/assets/design.png rename to comps/animation/wav2lip/assets/design.png diff --git a/comps/animation/assets/eg3_ref.wav b/comps/animation/wav2lip/assets/eg3_ref.wav similarity index 100% rename from comps/animation/assets/eg3_ref.wav rename to comps/animation/wav2lip/assets/eg3_ref.wav diff --git a/comps/animation/assets/flowchart.png b/comps/animation/wav2lip/assets/flowchart.png similarity index 100% rename from comps/animation/assets/flowchart.png rename to comps/animation/wav2lip/assets/flowchart.png diff --git a/comps/animation/assets/gaudi.png b/comps/animation/wav2lip/assets/gaudi.png similarity index 100% rename from comps/animation/assets/gaudi.png rename to comps/animation/wav2lip/assets/gaudi.png diff --git a/comps/animation/assets/opea_gh_qr.png 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index 4c9e96ecd2..55283bdb20 100644 --- a/comps/animation/test_animation_server.py +++ b/comps/animation/wav2lip/check_animation_server.py @@ -7,7 +7,7 @@ import requests ip_address = os.environ.get("ip_address") -endpoint = f"http://{ip_address}:7860/v1/animation" +endpoint = f"http://{ip_address}:9066/v1/animation" outfile = os.environ.get("OUTFILE") # Read the JSON file diff --git a/comps/animation/Dockerfile b/comps/animation/wav2lip/dependency/Dockerfile similarity index 81% rename from comps/animation/Dockerfile rename to comps/animation/wav2lip/dependency/Dockerfile index 358c09b03e..74a864c864 100644 --- a/comps/animation/Dockerfile +++ b/comps/animation/wav2lip/dependency/Dockerfile @@ -19,7 +19,7 @@ RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missin # Install GenAIComps RUN mkdir -p /home/user/comps COPY comps /home/user/comps -COPY comps/animation/entrypoint.sh /usr/local/bin/entrypoint.sh +COPY comps/animation/dependency/entrypoint.sh /usr/local/bin/entrypoint.sh # Install ppmpeg with x264 software codec RUN git clone https://github.com/FFmpeg/FFmpeg.git /home/user/comps/animation/FFmpeg @@ -30,17 +30,6 @@ RUN ./configure --enable-gpl --enable-libx264 && \ hash -r RUN chmod +x $(which ffmpeg) -# # Install Wav2Lip-GFPGAN from github -# RUN git clone https://github.com/ajay-sainy/Wav2Lip-GFPGAN.git /home/user/comps/animation/src -# # Rename subfolders "Wav2Lip" and "GFPGAN" -# RUN mv /home/user/comps/animation/src/Wav2Lip-master /home/user/comps/animation/src/Wav2Lip && \ -# mv /home/user/comps/animation/src/GFPGAN-master /home/user/comps/animation/src/GFPGAN - -# # Set the PYTHONPATH environment variable -# RUN touch /home/user/comps/animation/src/Wav2Lip/__init__.py -# RUN touch /home/user/comps/animation/src/GFPGAN/__init__.py -# ENV PYTHONPATH="/home/user/comps/animation/src:/home/user/comps/animation/src/Wav2Lip:/home/user/comps/animation/src/GFPGAN:$PYTHONPATH" - # Upgrade pip RUN python3 -m pip install --upgrade pip diff --git a/comps/animation/Dockerfile_hpu b/comps/animation/wav2lip/dependency/Dockerfile.intel_hpu similarity index 81% rename from comps/animation/Dockerfile_hpu rename to comps/animation/wav2lip/dependency/Dockerfile.intel_hpu index a5a12cbe13..b9d3f0acc4 100644 --- a/comps/animation/Dockerfile_hpu +++ b/comps/animation/wav2lip/dependency/Dockerfile.intel_hpu @@ -11,13 +11,15 @@ ENV PYTHON=/usr/bin/python3.10 RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \ yasm \ libx264-dev +RUN apt-get install -y nasm +RUN rm -rf /var/lib/apt/lists/* # TO-DO: Add a user for security # Install GenAIComps RUN mkdir -p /home/user/comps COPY comps /home/user/comps -COPY comps/animation/entrypoint.sh /usr/local/bin/entrypoint.sh +COPY comps/animation/dependency/entrypoint.sh /usr/local/bin/entrypoint.sh # Install ppmpeg with x264 software codec RUN git clone https://github.com/FFmpeg/FFmpeg.git /home/user/comps/animation/FFmpeg @@ -28,17 +30,6 @@ RUN ./configure --enable-gpl --enable-libx264 && \ hash -r RUN chmod +x $(which ffmpeg) -# # Install Wav2Lip-GFPGAN from github -# RUN git clone https://github.com/ajay-sainy/Wav2Lip-GFPGAN.git /home/user/comps/animation/src -# # Rename subfolders "Wav2Lip" and "GFPGAN" -# RUN mv /home/user/comps/animation/src/Wav2Lip-master /home/user/comps/animation/src/Wav2Lip && \ -# mv /home/user/comps/animation/src/GFPGAN-master /home/user/comps/animation/src/GFPGAN - -# # Set the PYTHONPATH environment variable -# RUN touch /home/user/comps/animation/src/Wav2Lip/__init__.py -# RUN touch /home/user/comps/animation/src/GFPGAN/__init__.py -# ENV PYTHONPATH="/home/user/comps/animation/src:/home/user/comps/animation/src/Wav2Lip:/home/user/comps/animation/src/GFPGAN:$PYTHONPATH" - # Upgrade pip RUN python3 -m pip install --upgrade pip diff --git a/comps/animation/wav2lip/dependency/__init__.py b/comps/animation/wav2lip/dependency/__init__.py new file mode 100644 index 0000000000..4582b4f9aa --- /dev/null +++ b/comps/animation/wav2lip/dependency/__init__.py @@ -0,0 +1,2 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 \ No newline at end of file diff --git a/comps/animation/wav2lip/dependency/check_wav2lip_server.py b/comps/animation/wav2lip/dependency/check_wav2lip_server.py new file mode 100644 index 0000000000..f05ec35535 --- /dev/null +++ b/comps/animation/wav2lip/dependency/check_wav2lip_server.py @@ -0,0 +1,19 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +import json +import os +import requests + +endpoint = f"http://localhost:7860/v1/wav2lip" +outfile = os.environ.get("OUTFILE") + +# Read the JSON file +with open("assets/audio/sample_question.json", "r") as file: + data = json.load(file) + +inputs = {"audio": data["byte_str"]} +response = requests.post(url=endpoint, + data=json.dumps(inputs), + proxies={"http": None}) +print(response.json()) diff --git a/comps/animation/entrypoint.sh b/comps/animation/wav2lip/dependency/entrypoint.sh similarity index 87% rename from comps/animation/entrypoint.sh rename to comps/animation/wav2lip/dependency/entrypoint.sh index e615506afc..5bd3d02d9c 100644 --- a/comps/animation/entrypoint.sh +++ b/comps/animation/wav2lip/dependency/entrypoint.sh @@ -7,9 +7,9 @@ export PT_HPU_LAZY_MODE=0 export PT_HPU_ENABLE_REFINE_DYNAMIC_SHAPES=1 # Wav2Lip, GFPGAN -python3 animation.py \ +python3 dependency/wav2lip_server.py \ --device $DEVICE \ ---port $((ANIMATION_PORT)) \ +--port $((WAV2LIP_PORT)) \ --inference_mode $INFERENCE_MODE \ --checkpoint_path $CHECKPOINT_PATH \ --face $FACE \ diff --git a/comps/animation/utils.py b/comps/animation/wav2lip/dependency/utils.py similarity index 95% rename from comps/animation/utils.py rename to comps/animation/wav2lip/dependency/utils.py index f87a7dfc35..dbf840f5dd 100644 --- a/comps/animation/utils.py +++ b/comps/animation/wav2lip/dependency/utils.py @@ -12,23 +12,22 @@ import os from glob import glob -# wav2lip from os import listdir, path - import cv2 - -# habana -import habana_frameworks.torch.core as htcore -import habana_frameworks.torch.hpu as hthpu import numpy as np import scipy import soundfile as sf import torch + +# wav2lip import Wav2Lip.audio as audio import Wav2Lip.face_detection as face_detection +# habana +import habana_frameworks.torch.core as htcore +import habana_frameworks.torch.hpu as hthpu + # gfpgan -from basicsr.utils import imwrite from gfpgan import GFPGANer from tqdm import tqdm from Wav2Lip.models import Wav2Lip @@ -38,13 +37,14 @@ def get_args(): parser = argparse.ArgumentParser(description="Inference code to lip-sync videos in the wild using Wav2Lip models") # General config parser.add_argument("--device", type=str, choices=["hpu", "cpu", "cuda"], default="cpu") + parser.add_argument("--host", type=str, default="0.0.0.0") parser.add_argument("--port", type=int, default=7860) parser.add_argument( "--inference_mode", type=str, - choices=["wav2clip_only", "wav2clip+gfpgan"], - default="wav2clip+gfpgan", - help="whether to use just wav2clip or include gfpgan", + choices=["wav2lip_only", "wav2lip+gfpgan"], + default="wav2lip+gfpgan", + help="whether to use just wav2lip or include gfpgan", ) # Wav2Lip config parser.add_argument( @@ -273,7 +273,7 @@ def _load(args): def load_model(args): model = Wav2Lip() print("Load checkpoint from: {}".format(args.checkpoint_path)) - checkpoint = _load(args.checkpoint_path) + checkpoint = _load(args) s = checkpoint["state_dict"] new_s = {} for k, v in s.items(): @@ -342,11 +342,7 @@ def load_gfpgan(args, bg_upsampler): device=args.device, ) - # torch.compile - # restorer.face_helper.face_det = torch.compile(restorer.face_helper.face_det, backend="hpu_backend") - # restorer.face_helper.face_parse = torch.compile(restorer.face_helper.face_parse, backend="hpu_backend") - # restorer.gfpgan = torch.compile(restorer.gfpgan, backend="hpu_backend") # some compilation issue - # print("Model GFPGAN and face helper compiled") + # Optional: torch.compile return restorer diff --git a/comps/animation/animation.py b/comps/animation/wav2lip/dependency/wav2lip_server.py similarity index 61% rename from comps/animation/animation.py rename to comps/animation/wav2lip/dependency/wav2lip_server.py index c45735adbc..2261c131be 100644 --- a/comps/animation/animation.py +++ b/comps/animation/wav2lip/dependency/wav2lip_server.py @@ -5,70 +5,35 @@ # Copyright (c) 2012, Anaconda, Inc. All rights reserved. import os -import pathlib -import platform -import subprocess -import time - import cv2 import ffmpeg import numpy as np +# FastAPI +import uvicorn +from fastapi import FastAPI, Request +from fastapi.responses import Response + # Wav2Lip-GFPGAN -import requests import Wav2Lip.audio as audio -import Wav2Lip.face_detection as face_detection -from basicsr.utils import imwrite -from gfpgan import GFPGANer -from Wav2Lip.models import Wav2Lip +from utils import * -cur_path = pathlib.Path(__file__).parent.resolve() -comps_path = os.path.join(cur_path, "../") -# Habana -import habana_frameworks.torch.core as htcore -import habana_frameworks.torch.hpu as hthpu -from utils import * +app = FastAPI() +model = None + +@app.get("/v1/health") +async def health() -> Response: + """Health check.""" + return Response(status_code=200) -# GenAIComps -from comps import ( - AnimationDoc, - Base64ByteStrDoc, - ServiceType, - opea_microservices, - register_microservice, - register_statistics, - statistics_dict, -) - -args = get_args() -print("args: ", args) - -# Specify device -if args.device == "hpu" and hthpu.is_available(): - device = "hpu" -elif args.device == "cuda": - device = "cuda" -elif args.device == "cpu": - device = "cpu" -else: - device = "cpu" - print("Invalid device argument, fall back to cpu") -print("Using {} for inference.".format(device)) - - -# Register the microservice -@register_microservice( - name="opea_service@animation", - service_type=ServiceType.ANIMATION, - endpoint="/v1/animation", - host="0.0.0.0", - port=args.port, - input_datatype=Base64ByteStrDoc, -) -@register_statistics(names=["opea_service@animation"]) -def animate(input: Base64ByteStrDoc): - start = time.time() +@app.post("/v1/wav2lip") +async def animate(request: Request): + print("Animation generation begins.") + + # Wait for request + request_dict = await request.json() + audio_b64_str = request_dict.pop("audio") if not os.path.exists("inputs"): os.makedirs("inputs") @@ -77,7 +42,6 @@ def animate(input: Base64ByteStrDoc): if not os.path.exists("outputs"): os.makedirs("outputs") - print(args.face, args.audio) if not os.path.isfile(args.face): raise ValueError("--face argument must be a valid path to video/image file") elif args.face.split(".")[-1] in ["jpg", "jpeg", "png"]: @@ -118,7 +82,7 @@ def animate(input: Base64ByteStrDoc): ffmpeg.input(args.audio).output("temp/temp.wav", strict="-2").run(overwrite_output=True) args.audio = "temp/temp.wav" else: - sr, y = base64_to_int16_to_wav(input.byte_str, "temp/temp.wav") + sr, y = base64_to_int16_to_wav(audio_b64_str, "temp/temp.wav") args.audio = "temp/temp.wav" wav = audio.load_wav(args.audio, 16000) @@ -128,7 +92,6 @@ def animate(input: Base64ByteStrDoc): raise ValueError("Mel contains nan! Using a TTS voice? Add a small epsilon noise to the wav file and try again") # one single video frame corresponds to 80/25*0.01 = 0.032 seconds (or 32 milliseconds) of audio - # 30 fps video will match closer to audio, than 25 fps mel_chunks = [] mel_idx_multiplier = 80.0 / fps i = 0 @@ -152,30 +115,8 @@ def animate(input: Base64ByteStrDoc): tqdm(gen, total=int(np.ceil(float(len(mel_chunks)) / batch_size))) ): if i == 0: - # load Wav2Lip model - model = load_model(args) - print("Wav2Lip Model loaded") - - # load BG sampler if needed - if args.inference_mode == "wav2clip+gfpgan" and args.bg_upsampler == "realesrgan": - model_bg_upsampler = load_bg_upsampler(args) - print("Model BG Sampler loaded") - # model_bg_upsampler = torch.compile(model_bg_upsampler, backend="hpu_backend") - # print("Model BG Sampler compiled") - else: - model_bg_upsampler = None - print("Model BG Sampler not loaded") - - # load GFPGAN model if needed - if args.inference_mode == "wav2clip+gfpgan": - model_restorer = load_gfpgan(args, model_bg_upsampler) - print("Model GFPGAN and face helper loaded") - else: - model_restorer = None - print("Model GFPGAN not loaded") - frame_h, frame_w = full_frames[0].shape[:-1] - if args.inference_mode == "wav2clip_only": + if args.inference_mode == "wav2lip_only": out = cv2.VideoWriter("temp/result.avi", cv2.VideoWriter_fourcc(*"DIVX"), fps, (frame_w, frame_h)) else: out = cv2.VideoWriter( @@ -199,18 +140,13 @@ def animate(input: Base64ByteStrDoc): f[y1:y2, x1:x2] = p # patching # restore faces and background if necessary - if args.inference_mode == "wav2clip+gfpgan": + if args.inference_mode == "wav2lip+gfpgan": cropped_faces, restored_faces, f = model_restorer.enhance( f, has_aligned=args.aligned, only_center_face=args.only_center_face, paste_back=True ) out.write(f) out.release() - # command = "ffmpeg -y -i {} -i {} -strict -2 -c:v libx264 -crf 23 -preset medium -c:a aac {}".format( - # args.audio, "temp/result.avi", args.outfile - # ) - # subprocess.call(command, shell=platform.system() != "Windows") - ffmpeg.output( ffmpeg.input(args.audio), ffmpeg.input("temp/result.avi"), @@ -222,11 +158,46 @@ def animate(input: Base64ByteStrDoc): acodec="aac", ).run(overwrite_output=True) - statistics_dict["opea_service@animation"].append_latency(time.time() - start, None) - # return_str = f"Video generated successfully, check {args.outfile} for the result." - return AnimationDoc(video_save_path=args.outfile) + return {"wav2lip_result": args.outfile} if __name__ == "__main__": - print("Animation initialized.") - opea_microservices["opea_service@animation"].start() + # Load arguments + args = get_args() + print("args: ", args) + + # Specify device + # Habana + import habana_frameworks.torch.hpu as hthpu + if args.device == "hpu" and hthpu.is_available(): + device = "hpu" + elif args.device == "cuda": + device = "cuda" + elif args.device == "cpu": + device = "cpu" + else: + device = "cpu" + print("Invalid device argument, fall back to cpu") + print("Using {} for inference.".format(device)) + + # Load Wav2Lip, BG sampler, GFPGAN models + model = load_model(args) + print("Wav2Lip Model loaded") + + if args.inference_mode == "wav2lip+gfpgan" and args.bg_upsampler == "realesrgan": + model_bg_upsampler = load_bg_upsampler(args) + print("Model BG Sampler loaded") + else: + model_bg_upsampler = None + print("Model BG Sampler not loaded") + + # load GFPGAN model if needed + if args.inference_mode == "wav2lip+gfpgan": + model_restorer = load_gfpgan(args, model_bg_upsampler) + print("Model GFPGAN and face helper loaded") + else: + model_restorer = None + print("Model GFPGAN not loaded") + + # Run FastAPI + uvicorn.run(app, host=args.host, port=args.port) \ No newline at end of file diff --git a/comps/animation/docker_run.sh b/comps/animation/wav2lip/docker_run.sh similarity index 100% rename from comps/animation/docker_run.sh rename to comps/animation/wav2lip/docker_run.sh diff --git a/comps/animation/requirements.txt b/comps/animation/wav2lip/requirements.txt similarity index 95% rename from comps/animation/requirements.txt rename to comps/animation/wav2lip/requirements.txt index 54eac78bec..87fdd5e81d 100644 --- a/comps/animation/requirements.txt +++ b/comps/animation/wav2lip/requirements.txt @@ -14,13 +14,14 @@ numpy==1.23.5 opencv-python==4.10.0.84 # used by cloud native +aiohttp opentelemetry-api opentelemetry-exporter-otlp opentelemetry-sdk pandas prometheus_fastapi_instrumentator pyarrow -pydantic #==1.10.13 +pydantic pyyaml==6.0 scipy==1.14.0 shortuuid diff --git a/comps/cores/mega/constants.py b/comps/cores/mega/constants.py index 7536024c37..fded5e8114 100644 --- a/comps/cores/mega/constants.py +++ b/comps/cores/mega/constants.py @@ -31,6 +31,7 @@ class ServiceType(Enum): WEB_RETRIEVER = 14 IMAGE2VIDEO = 15 TEXT2IMAGE = 16 + ANIMATION = 17 class MegaServiceEndpoint(Enum): From d058496ad15c486c6cd1e0b60c27eaf607bb0e22 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 15 Oct 2024 22:35:46 +0000 Subject: [PATCH 21/49] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- comps/animation/wav2lip/README.md | 4 ++++ comps/animation/wav2lip/animation.py | 7 +++++-- comps/animation/wav2lip/dependency/__init__.py | 2 +- .../wav2lip/dependency/check_wav2lip_server.py | 7 +++---- comps/animation/wav2lip/dependency/utils.py | 10 +++++----- comps/animation/wav2lip/dependency/wav2lip_server.py | 11 +++++++---- comps/animation/wav2lip/requirements.txt | 6 +++--- 7 files changed, 28 insertions(+), 19 deletions(-) diff --git a/comps/animation/wav2lip/README.md b/comps/animation/wav2lip/README.md index 1c022a863b..ef8b34935a 100644 --- a/comps/animation/wav2lip/README.md +++ b/comps/animation/wav2lip/README.md @@ -26,6 +26,7 @@ docker build -t opea/wav2lip-gaudi:latest -f comps/animation/dependency/Dockerfi ``` ### 1.1.2 Animation server image + ```bash docker build -t opea/animation:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/animation/wav2lip/Dockerfile . ``` @@ -51,7 +52,9 @@ export FPS=10 ``` # 🚀2. Run the Docker container + ## 2.1 Run Wav2Lip Microservice + - Xeon CPU ```bash @@ -65,6 +68,7 @@ docker run --privileged -d --runtime=habana --cap-add=sys_nice --net=host --ipc= ``` ## 2.2 Run Animation Microservice + ```bash docker run -d -p 9066:9066 --ipc=host --name "animation-service" -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e WAV2LIP_ENDPOINT=http://$ip_address:7860 opea/animation:latest ``` diff --git a/comps/animation/wav2lip/animation.py b/comps/animation/wav2lip/animation.py index 05ee97f271..bacf6b45f2 100644 --- a/comps/animation/wav2lip/animation.py +++ b/comps/animation/wav2lip/animation.py @@ -4,25 +4,28 @@ # Copyright 1999-2003 The OpenLDAP Foundation, Redwood City, California, USA. All Rights Reserved. # Copyright (c) 2012, Anaconda, Inc. All rights reserved. +import json import os import time -import json + import requests # GenAIComps from comps import CustomLogger + logger = CustomLogger("animation") logflag = os.getenv("LOGFLAG", False) from comps import ( Base64ByteStrDoc, - VideoPath, ServiceType, + VideoPath, opea_microservices, register_microservice, register_statistics, statistics_dict, ) + # Register the microservice @register_microservice( name="opea_service@animation", diff --git a/comps/animation/wav2lip/dependency/__init__.py b/comps/animation/wav2lip/dependency/__init__.py index 4582b4f9aa..916f3a44b2 100644 --- a/comps/animation/wav2lip/dependency/__init__.py +++ b/comps/animation/wav2lip/dependency/__init__.py @@ -1,2 +1,2 @@ # Copyright (C) 2024 Intel Corporation -# SPDX-License-Identifier: Apache-2.0 \ No newline at end of file +# SPDX-License-Identifier: Apache-2.0 diff --git a/comps/animation/wav2lip/dependency/check_wav2lip_server.py b/comps/animation/wav2lip/dependency/check_wav2lip_server.py index f05ec35535..729d7cda85 100644 --- a/comps/animation/wav2lip/dependency/check_wav2lip_server.py +++ b/comps/animation/wav2lip/dependency/check_wav2lip_server.py @@ -3,9 +3,10 @@ import json import os + import requests -endpoint = f"http://localhost:7860/v1/wav2lip" +endpoint = "http://localhost:7860/v1/wav2lip" outfile = os.environ.get("OUTFILE") # Read the JSON file @@ -13,7 +14,5 @@ data = json.load(file) inputs = {"audio": data["byte_str"]} -response = requests.post(url=endpoint, - data=json.dumps(inputs), - proxies={"http": None}) +response = requests.post(url=endpoint, data=json.dumps(inputs), proxies={"http": None}) print(response.json()) diff --git a/comps/animation/wav2lip/dependency/utils.py b/comps/animation/wav2lip/dependency/utils.py index dbf840f5dd..c209a954a8 100644 --- a/comps/animation/wav2lip/dependency/utils.py +++ b/comps/animation/wav2lip/dependency/utils.py @@ -11,9 +11,13 @@ import io import os from glob import glob - from os import listdir, path + import cv2 + +# habana +import habana_frameworks.torch.core as htcore +import habana_frameworks.torch.hpu as hthpu import numpy as np import scipy import soundfile as sf @@ -23,10 +27,6 @@ import Wav2Lip.audio as audio import Wav2Lip.face_detection as face_detection -# habana -import habana_frameworks.torch.core as htcore -import habana_frameworks.torch.hpu as hthpu - # gfpgan from gfpgan import GFPGANer from tqdm import tqdm diff --git a/comps/animation/wav2lip/dependency/wav2lip_server.py b/comps/animation/wav2lip/dependency/wav2lip_server.py index 2261c131be..e4edb99590 100644 --- a/comps/animation/wav2lip/dependency/wav2lip_server.py +++ b/comps/animation/wav2lip/dependency/wav2lip_server.py @@ -5,28 +5,30 @@ # Copyright (c) 2012, Anaconda, Inc. All rights reserved. import os + import cv2 import ffmpeg import numpy as np # FastAPI import uvicorn -from fastapi import FastAPI, Request -from fastapi.responses import Response # Wav2Lip-GFPGAN import Wav2Lip.audio as audio +from fastapi import FastAPI, Request +from fastapi.responses import Response from utils import * - app = FastAPI() model = None + @app.get("/v1/health") async def health() -> Response: """Health check.""" return Response(status_code=200) + @app.post("/v1/wav2lip") async def animate(request: Request): print("Animation generation begins.") @@ -169,6 +171,7 @@ async def animate(request: Request): # Specify device # Habana import habana_frameworks.torch.hpu as hthpu + if args.device == "hpu" and hthpu.is_available(): device = "hpu" elif args.device == "cuda": @@ -200,4 +203,4 @@ async def animate(request: Request): print("Model GFPGAN not loaded") # Run FastAPI - uvicorn.run(app, host=args.host, port=args.port) \ No newline at end of file + uvicorn.run(app, host=args.host, port=args.port) diff --git a/comps/animation/wav2lip/requirements.txt b/comps/animation/wav2lip/requirements.txt index 87fdd5e81d..a93f53083f 100644 --- a/comps/animation/wav2lip/requirements.txt +++ b/comps/animation/wav2lip/requirements.txt @@ -1,3 +1,6 @@ + +# used by cloud native +aiohttp basicsr==1.4.2 docarray[full] facexlib==0.3.0 @@ -12,9 +15,6 @@ lmdb==1.5.1 numba==0.60.0 numpy==1.23.5 opencv-python==4.10.0.84 - -# used by cloud native -aiohttp opentelemetry-api opentelemetry-exporter-otlp opentelemetry-sdk From ff0ec2d19fc50ccb9638836e11caf7dec134c45b Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Wed, 16 Oct 2024 03:04:22 +0000 Subject: [PATCH 22/49] Add dockerfiles in animation-compose-cd.yaml Signed-off-by: Chun Tao --- .../docker/compose/animation-compose-cd.yaml | 18 ++++++++++++++++++ comps/animation/wav2lip/Dockerfile | 2 +- 2 files changed, 19 insertions(+), 1 deletion(-) create mode 100644 .github/workflows/docker/compose/animation-compose-cd.yaml diff --git a/.github/workflows/docker/compose/animation-compose-cd.yaml b/.github/workflows/docker/compose/animation-compose-cd.yaml new file mode 100644 index 0000000000..2c37265289 --- /dev/null +++ b/.github/workflows/docker/compose/animation-compose-cd.yaml @@ -0,0 +1,18 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +# this file should be run in the root of the repo +# images used by GenAIExamples: animation, wav2lip, wav2lip-gaudi +services: + animation: + build: + dockerfile: comps/animation/wav2lip/Dockerfile + image: ${REGISTRY:-opea}/animation:${TAG:-latest} + wav2lip: + build: + dockerfile: comps/animation/wav2lip/dependency/Dockerfile + image: ${REGISTRY:-opea}/wav2lip:${TAG:-latest} + wav2lip-gaudi: + build: + dockerfile: comps/animation/wav2lip/dependency/Dockerfile.intel_hpu + image: ${REGISTRY:-opea}/wav2lip-gaudi:${TAG:-latest} \ No newline at end of file diff --git a/comps/animation/wav2lip/Dockerfile b/comps/animation/wav2lip/Dockerfile index e32aa9cc62..c03a0fd4a7 100644 --- a/comps/animation/wav2lip/Dockerfile +++ b/comps/animation/wav2lip/Dockerfile @@ -21,4 +21,4 @@ ENV PYTHONPATH=$PYTHONPATH:/home/user WORKDIR /home/user/comps/animation -ENTRYPOINT ["python", "animation.py"] \ No newline at end of file +ENTRYPOINT ["python3", "animation.py"] \ No newline at end of file From 87f51e61f480e9eceeca4dc629f59795439c56c3 Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Wed, 16 Oct 2024 03:12:02 +0000 Subject: [PATCH 23/49] Fix end of file issue in animation-compose-cd.yaml Signed-off-by: Chun Tao --- .github/workflows/docker/compose/animation-compose-cd.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/docker/compose/animation-compose-cd.yaml b/.github/workflows/docker/compose/animation-compose-cd.yaml index 2c37265289..98b85cacf0 100644 --- a/.github/workflows/docker/compose/animation-compose-cd.yaml +++ b/.github/workflows/docker/compose/animation-compose-cd.yaml @@ -15,4 +15,4 @@ services: wav2lip-gaudi: build: dockerfile: comps/animation/wav2lip/dependency/Dockerfile.intel_hpu - image: ${REGISTRY:-opea}/wav2lip-gaudi:${TAG:-latest} \ No newline at end of file + image: ${REGISTRY:-opea}/wav2lip-gaudi:${TAG:-latest} From c4f43da8f26e237ee472d1334e2afa000acce684 Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Fri, 18 Oct 2024 06:26:10 +0000 Subject: [PATCH 24/49] Fix Docker deployment on Xeon Signed-off-by: Chun Tao --- comps/animation/wav2lip/README.md | 15 ++--- comps/animation/wav2lip/assets/design.png | Bin 173014 -> 0 bytes .../wav2lip/assets/outputs/result.mp4 | Bin 225848 -> 226624 bytes comps/animation/wav2lip/dependency/Dockerfile | 54 +++++++++--------- .../wav2lip/dependency/Dockerfile.intel_hpu | 12 ++-- .../wav2lip/dependency/download_ckpts.sh | 5 ++ .../wav2lip/dependency/entrypoint.sh | 2 + comps/animation/wav2lip/dependency/utils.py | 19 +++--- .../wav2lip/dependency/wav2lip_server.py | 13 +++-- 9 files changed, 65 insertions(+), 55 deletions(-) delete mode 100644 comps/animation/wav2lip/assets/design.png create mode 100644 comps/animation/wav2lip/dependency/download_ckpts.sh diff --git a/comps/animation/wav2lip/README.md b/comps/animation/wav2lip/README.md index ef8b34935a..64ec4328b7 100644 --- a/comps/animation/wav2lip/README.md +++ b/comps/animation/wav2lip/README.md @@ -16,13 +16,13 @@ cd GenAIComps - Xeon CPU ```bash -docker build -t opea/wav2lip:latest -f comps/animation/dependency/Dockerfile . +docker build -t opea/wav2lip:latest -f comps/animation/wav2lip/dependency/Dockerfile . ``` - Gaudi2 HPU ```bash -docker build -t opea/wav2lip-gaudi:latest -f comps/animation/dependency/Dockerfile.intel_hpu . +docker build -t opea/wav2lip-gaudi:latest -f comps/animation/wav2lip/dependency/Dockerfile.intel_hpu . ``` ### 1.1.2 Animation server image @@ -35,18 +35,19 @@ docker build -t opea/animation:latest --build-arg https_proxy=$https_proxy --bui ```bash export ip_address=$(hostname -I | awk '{print $1}') -# export DEVICE="cpu" -export DEVICE="hpu" +export DEVICE="cpu" +# export DEVICE="hpu" export WAV2LIP_PORT=7860 export ANIMATION_PORT=9066 export INFERENCE_MODE='wav2lip+gfpgan' -export CHECKPOINT_PATH='/usr/local/lib/python3.10/dist-packages/Wav2Lip/checkpoints/wav2lip_gan.pth' +export CHECKPOINT_PATH='/usr/local/lib/python3.11/site-packages/Wav2Lip/checkpoints/wav2lip_gan.pth' +# export CHECKPOINT_PATH='/usr/local/lib/python3.10/dist-packages/Wav2Lip/checkpoints/wav2lip_gan.pth' export FACE='assets/avatar1.jpg' # export AUDIO='assets/eg3_ref.wav' # audio file path is optional, will use base64str in the post request as input if is 'None' export AUDIO='None' export FACESIZE=96 export OUTFILE="$(pwd)/comps/animation/wav2lip/assets/outputs/result.mp4" -export GFPGAN_MODEL_VERSION=1.3 +export GFPGAN_MODEL_VERSION=1.4 # latest version, can roll back to v1.3 if needed export UPSCALE_FACTOR=1 export FPS=10 ``` @@ -64,7 +65,7 @@ docker run --privileged -d -p 7860:7860 --ipc=host --name "wav2lip-service" -v / - Gaudi2 HPU ```bash -docker run --privileged -d --runtime=habana --cap-add=sys_nice --net=host --ipc=host --name "wav2lip-gaudi-service" -v /var/run/docker.sock:/var/run/docker.sock -v /usr/bin/docker:/usr/bin/docker -v $(pwd):$(pwd) -w /home/user/comps/animation -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e PYTHON=/usr/bin/python3.10 -e DEVICE=$DEVICE -e INFERENCE_MODE=$INFERENCE_MODE -e CHECKPOINT_PATH=$CHECKPOINT_PATH -e FACE=$FACE -e AUDIO=$AUDIO -e FACESIZE=$FACESIZE -e OUTFILE=$OUTFILE -e GFPGAN_MODEL_VERSION=$GFPGAN_MODEL_VERSION -e UPSCALE_FACTOR=$UPSCALE_FACTOR -e FPS=$FPS -e WAV2LIP_PORT=$WAV2LIP_PORT opea/wav2lip-gaudi:latest +docker run --privileged -d -p 7860:7860 --runtime=habana --cap-add=sys_nice --net=host --ipc=host --name "wav2lip-gaudi-service" -v /var/run/docker.sock:/var/run/docker.sock -v /usr/bin/docker:/usr/bin/docker -v $(pwd):$(pwd) -w /home/user/comps/animation -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e PYTHON=/usr/bin/python3.10 -e DEVICE=$DEVICE -e INFERENCE_MODE=$INFERENCE_MODE -e CHECKPOINT_PATH=$CHECKPOINT_PATH -e FACE=$FACE -e AUDIO=$AUDIO -e FACESIZE=$FACESIZE -e OUTFILE=$OUTFILE -e GFPGAN_MODEL_VERSION=$GFPGAN_MODEL_VERSION -e UPSCALE_FACTOR=$UPSCALE_FACTOR -e FPS=$FPS -e WAV2LIP_PORT=$WAV2LIP_PORT opea/wav2lip-gaudi:latest ``` ## 2.2 Run Animation Microservice diff --git a/comps/animation/wav2lip/assets/design.png b/comps/animation/wav2lip/assets/design.png deleted file mode 100644 index 1368137d6d2d12c6fbc0a7b5a18b5bf08bcc9b0e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 173014 zcmeFYcTiJZ-!`hC6s36s(gFx5z1KvVNVfn{dW(ScPNX*x1PzdYNReIz=}4C@hR_2@ z?+_qJ?-1(QKJRnpyTAG7{C{T7nPCr;mA!Y?Ui(+C>slXmwA3kYG2gm&?HZ+~hRTa; 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zMzIub)(`OC62f&W0HELfSA(Oka4|qKayxa)aL2&(_j==u}_zHXRgb+e~)8OdV;ONHS z=zhS_EyB^6!O^|I(V4>0Rlw1)z|rl((LKP?@xal2g`?YpqdNk?QgAe?&2V&`aC9+n zblz}uLIBvd5&*kh0pLI)0381WfHUCe3*G|Y3IhP#77l>>bOG=v8K4ZFz6QX1!2tL) z5CGr6`95y|5DZ-aLYNIeDD(gb0|@}(9swW%dvI)UJd$vOC}_gb`NGl30}wTMSzR5D L&IY#b1u_4BMUfcO diff --git a/comps/animation/wav2lip/dependency/Dockerfile b/comps/animation/wav2lip/dependency/Dockerfile index 74a864c864..5c5c086d2c 100644 --- a/comps/animation/wav2lip/dependency/Dockerfile +++ b/comps/animation/wav2lip/dependency/Dockerfile @@ -6,25 +6,31 @@ FROM python:3.11-slim # Set environment variables ENV LANG=en_US.UTF-8 -ENV PYTHONPATH=/home:/home/user +ENV PYTHONPATH=/usr/local/lib/python3.11/site-packages:/home:/home/user ENV PYTHON=/usr/bin/python3.11 # Install dependencies RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \ yasm \ - libx264-dev - -# TO-DO: Add a user for security + build-essential \ + pkg-config \ + libx264-dev \ + git \ + nasm \ + wget \ + libgl1-mesa-glx \ + libglib2.0-0 \ + && rm -rf /var/lib/apt/lists/* # Install GenAIComps RUN mkdir -p /home/user/comps COPY comps /home/user/comps -COPY comps/animation/dependency/entrypoint.sh /usr/local/bin/entrypoint.sh +COPY comps/animation/wav2lip/dependency/entrypoint.sh /usr/local/bin/entrypoint.sh -# Install ppmpeg with x264 software codec -RUN git clone https://github.com/FFmpeg/FFmpeg.git /home/user/comps/animation/FFmpeg -WORKDIR /home/user/comps/animation/FFmpeg -RUN ./configure --enable-gpl --enable-libx264 && \ +# Install ffmpeg with x264 software codec +RUN git clone https://github.com/FFmpeg/FFmpeg.git /home/user/comps/animation/wav2lip/FFmpeg +WORKDIR /home/user/comps/animation/wav2lip/FFmpeg +RUN ./configure --enable-gpl --enable-libx264 --enable-cross-compile && \ make -j$(nproc-1) && \ make install && \ hash -r @@ -35,40 +41,34 @@ RUN python3 -m pip install --upgrade pip # Install Wav2Lip from pip RUN pip install --no-deps Wav2Lipy -RUN rm /usr/local/lib/python3.10/dist-packages/Wav2Lip/__init__.py && touch /usr/local/lib/python3.10/dist-packages/Wav2Lip/__init__.py -ENV PYTHONPATH="/usr/local/lib/python3.10/dist-packages/Wav2Lip:$PYTHONPATH" +RUN rm /usr/local/lib/python3.11/site-packages/Wav2Lip/__init__.py && touch /usr/local/lib/python3.11/site-packages/Wav2Lip/__init__.py +ENV PYTHONPATH="$PYTHONPATH:/usr/local/lib/python3.11/site-packages/Wav2Lip" # Install GFPGAN from pip RUN pip install --no-deps gfpgan -RUN touch /usr/local/lib/python3.10/dist-packages/gfpgan/__init__.py -ENV PYTHONPATH="/usr/local/lib/python3.10/dist-packages/gfpgan:$PYTHONPATH" +RUN touch /usr/local/lib/python3.11/site-packages/gfpgan/__init__.py +ENV PYTHONPATH="$PYTHONPATH:/usr/local/lib/python3.11/site-packages/gfpgan" # Download pre-trained models -# Install gdown -RUN pip install gdown -WORKDIR /usr/local/lib/python3.10/dist-packages -# RUN wget https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth -O Wav2Lip/face_detection/detection/sfd/s3fd.pth -RUN gdown https://drive.google.com/uc?id=1wwts8U4Wx2lpLOI9uGcUB-TLmKRIFnP8 -O Wav2Lip/face_detection/detection/sfd/s3fd.pth -RUN mkdir -p Wav2Lip/checkpoints -RUN gdown https://drive.google.com/uc?id=1mIKfu_onFKbkbrq6cKVRBblXe5bNMhd9 -O Wav2Lip/checkpoints/wav2lip.pth -RUN gdown https://drive.google.com/uc?id=1DD7dtUfNWqNoW-2Gnp78B4PyaiJ5ybhP -O Wav2Lip/checkpoints/wav2lip_gan.pth -RUN wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P gfpgan/experiments/pretrained_models +WORKDIR /usr/local/lib/python3.11/site-packages +RUN chmod +x /home/user/comps/animation/wav2lip/dependency/download_ckpts.sh +RUN /home/user/comps/animation/wav2lip/dependency/download_ckpts.sh # Install pip dependencies -RUN pip install -r /home/user/comps/animation/requirements.txt +RUN pip install -r /home/user/comps/animation/wav2lip/requirements.txt # Custom patches # Modify the degradations.py file to import rgb_to_grayscale from torchvision.transforms.functional -RUN sed -i 's/from torchvision.transforms.functional_tensor import rgb_to_grayscale/from torchvision.transforms.functional import rgb_to_grayscale/' /usr/local/lib/python3.10/dist-packages/basicsr/data/degradations.py +RUN sed -i 's/from torchvision.transforms.functional_tensor import rgb_to_grayscale/from torchvision.transforms.functional import rgb_to_grayscale/' /usr/local/lib/python3.11/site-packages/basicsr/data/degradations.py # Modify the core.py file to include 'hpu' in the device check -RUN sed -i "s/if 'cpu' not in device and 'cuda' not in device:/if 'cpu' not in device and 'cuda' not in device and 'hpu' not in device:/" /usr/local/lib/python3.10/dist-packages/Wav2Lip/face_detection/detection/core.py +RUN sed -i "s/if 'cpu' not in device and 'cuda' not in device:/if 'cpu' not in device and 'cuda' not in device and 'hpu' not in device:/" /usr/local/lib/python3.11/site-packages/Wav2Lip/face_detection/detection/core.py # To be compatible with librosa==0.10.2, instead of librosa==0.7.0 because the co-dependency numba==0.48 cannot be pip installed -RUN sed -i 's/hp.sample_rate, hp.n_fft/sr=hp.sample_rate, n_fft=hp.n_fft/' /usr/local/lib/python3.10/dist-packages/Wav2Lip/audio.py +RUN sed -i 's/hp.sample_rate, hp.n_fft/sr=hp.sample_rate, n_fft=hp.n_fft/' /usr/local/lib/python3.11/site-packages/Wav2Lip/audio.py # Set the working directory -WORKDIR /home/user/comps/animation +WORKDIR /home/user/comps/animation/wav2lip/ # Define the command to run when the container starts RUN chmod +x /usr/local/bin/entrypoint.sh diff --git a/comps/animation/wav2lip/dependency/Dockerfile.intel_hpu b/comps/animation/wav2lip/dependency/Dockerfile.intel_hpu index b9d3f0acc4..5439e9d9e1 100644 --- a/comps/animation/wav2lip/dependency/Dockerfile.intel_hpu +++ b/comps/animation/wav2lip/dependency/Dockerfile.intel_hpu @@ -4,24 +4,24 @@ FROM vault.habana.ai/gaudi-docker/1.16.2/ubuntu22.04/habanalabs/pytorch-installe # Set environment variables ENV LANG=en_US.UTF-8 -ENV PYTHONPATH=/home:/home/user:/usr/lib/habanalabs/:/optimum-habana +ENV PYTHONPATH=/usr/local/lib/python3.10/dist-packages:/home:/home/user:/usr/lib/habanalabs/:/optimum-habana ENV PYTHON=/usr/bin/python3.10 # Install dependencies RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \ yasm \ + build-essential \ + pkg-config \ libx264-dev RUN apt-get install -y nasm RUN rm -rf /var/lib/apt/lists/* -# TO-DO: Add a user for security - # Install GenAIComps RUN mkdir -p /home/user/comps COPY comps /home/user/comps COPY comps/animation/dependency/entrypoint.sh /usr/local/bin/entrypoint.sh -# Install ppmpeg with x264 software codec +# Install ffmpeg with x264 software codec RUN git clone https://github.com/FFmpeg/FFmpeg.git /home/user/comps/animation/FFmpeg WORKDIR /home/user/comps/animation/FFmpeg RUN ./configure --enable-gpl --enable-libx264 && \ @@ -36,12 +36,12 @@ RUN python3 -m pip install --upgrade pip # Install Wav2Lip from pip RUN pip install --no-deps Wav2Lipy RUN rm /usr/local/lib/python3.10/dist-packages/Wav2Lip/__init__.py && touch /usr/local/lib/python3.10/dist-packages/Wav2Lip/__init__.py -ENV PYTHONPATH="/usr/local/lib/python3.10/dist-packages/Wav2Lip:$PYTHONPATH" +ENV PYTHONPATH="$PYTHONPATH:/usr/local/lib/python3.10/dist-packages/Wav2Lip" # Install GFPGAN from pip RUN pip install --no-deps gfpgan RUN touch /usr/local/lib/python3.10/dist-packages/gfpgan/__init__.py -ENV PYTHONPATH="/usr/local/lib/python3.10/dist-packages/gfpgan:$PYTHONPATH" +ENV PYTHONPATH="$PYTHONPATH:/usr/local/lib/python3.10/dist-packages/gfpgan" # Download pre-trained models # Install gdown diff --git a/comps/animation/wav2lip/dependency/download_ckpts.sh b/comps/animation/wav2lip/dependency/download_ckpts.sh new file mode 100644 index 0000000000..ecbb7a4c2b --- /dev/null +++ b/comps/animation/wav2lip/dependency/download_ckpts.sh @@ -0,0 +1,5 @@ +wget https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth -O Wav2Lip/face_detection/detection/sfd/s3fd.pth +mkdir -p Wav2Lip/checkpoints +wget "https://iiitaphyd-my.sharepoint.com/:f:/g/personal/radrabha_m_research_iiit_ac_in/Eb3LEzbfuKlJiR600lQWRxgBIY27JZg80f7V9jtMfbNDaQ?download=1" -O Wav2Lip/checkpoints/wav2lip.pth +wget "https://iiitaphyd-my.sharepoint.com/:f:/g/personal/radrabha_m_research_iiit_ac_in/EdjI7bZlgApMqsVoEUUXpLsBxqXbn5z8VTmoxp55YNDcIA?download=1" -O Wav2Lip/checkpoints/wav2lip_gan.pth +wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P gfpgan/experiments/pretrained_models \ No newline at end of file diff --git a/comps/animation/wav2lip/dependency/entrypoint.sh b/comps/animation/wav2lip/dependency/entrypoint.sh index 5bd3d02d9c..0bf6798fe5 100644 --- a/comps/animation/wav2lip/dependency/entrypoint.sh +++ b/comps/animation/wav2lip/dependency/entrypoint.sh @@ -1,4 +1,6 @@ #!/bin/sh +cd /home/user/comps/animation/wav2lip/ || exit + # Copyright (C) 2024 Intel Corporation # SPDX-License-Identifier: Apache-2.0 diff --git a/comps/animation/wav2lip/dependency/utils.py b/comps/animation/wav2lip/dependency/utils.py index c209a954a8..a2fa68c482 100644 --- a/comps/animation/wav2lip/dependency/utils.py +++ b/comps/animation/wav2lip/dependency/utils.py @@ -14,12 +14,7 @@ from os import listdir, path import cv2 - -# habana -import habana_frameworks.torch.core as htcore -import habana_frameworks.torch.hpu as hthpu import numpy as np -import scipy import soundfile as sf import torch @@ -126,7 +121,7 @@ def get_args(): # we use version to select models, which is more user-friendly parser.add_argument("--img_size", type=int, default=96, help="size to reshape the detected face") parser.add_argument( - "-v", "--version", type=str, default="1.3", help="GFPGAN model version. Option: 1 | 1.2 | 1.3. Default: 1.3" + "-v", "--version", type=str, default="1.4", help="GFPGAN model version. Option: 1 | 1.2 | 1.3 | 1.4. Default: 1.4" ) parser.add_argument( "-s", "--upscale", type=int, default=2, help="The final upsampling scale of the image. Default: 2" @@ -283,7 +278,8 @@ def load_model(args): def load_bg_upsampler(args): - if (not torch.cuda.is_available()) or (not hthpu.is_available()): # CPU + # if (not torch.cuda.is_available()) or (not hthpu.is_available()): # CPU + if args.device == "cpu": import warnings warnings.warn( @@ -321,15 +317,20 @@ def load_gfpgan(args, bg_upsampler): arch = "clean" channel_multiplier = 2 model_name = "GFPGANv1.3" + elif args.version == "1.4": + arch = "clean" + channel_multiplier = 2 + model_name = "GFPGANv1.4" else: raise ValueError(f"Wrong model version {args.version}.") # determine model path + pythonpath = os.environ.get('PYTHONPATH').split(':')[0] model_path = path.join( - "/usr/local/lib/python3.10/dist-packages/gfpgan/experiments/pretrained_models", model_name + ".pth" + pythonpath, "gfpgan/experiments/pretrained_models", model_name + ".pth" ) if not path.isfile(model_path): - model_path = path.join("usr/local/lib/python3.10/dist-packages/gfpgan/realesrgan/weights", model_name + ".pth") + model_path = path.join(pythonpath, "gfpgan/realesrgan/weights", model_name + ".pth") if not path.isfile(model_path): raise ValueError(f"Model {model_name} does not exist") diff --git a/comps/animation/wav2lip/dependency/wav2lip_server.py b/comps/animation/wav2lip/dependency/wav2lip_server.py index e4edb99590..3ba9416220 100644 --- a/comps/animation/wav2lip/dependency/wav2lip_server.py +++ b/comps/animation/wav2lip/dependency/wav2lip_server.py @@ -170,14 +170,15 @@ async def animate(request: Request): # Specify device # Habana - import habana_frameworks.torch.hpu as hthpu - - if args.device == "hpu" and hthpu.is_available(): - device = "hpu" + if args.device == "hpu": + import habana_frameworks.torch.hpu as hthpu + import habana_frameworks.torch.core as htcore + if hthpu.is_available(): + device = "hpu" + else: + device = "cpu" elif args.device == "cuda": device = "cuda" - elif args.device == "cpu": - device = "cpu" else: device = "cpu" print("Invalid device argument, fall back to cpu") From b7752b53f0e7fa47af765d06ce4357737ec69c3b Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 18 Oct 2024 06:27:37 +0000 Subject: [PATCH 25/49] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- comps/animation/wav2lip/dependency/download_ckpts.sh | 5 ++++- comps/animation/wav2lip/dependency/utils.py | 12 +++++++----- comps/animation/wav2lip/dependency/wav2lip_server.py | 3 ++- 3 files changed, 13 insertions(+), 7 deletions(-) diff --git a/comps/animation/wav2lip/dependency/download_ckpts.sh b/comps/animation/wav2lip/dependency/download_ckpts.sh index ecbb7a4c2b..a729c8ffd6 100644 --- a/comps/animation/wav2lip/dependency/download_ckpts.sh +++ b/comps/animation/wav2lip/dependency/download_ckpts.sh @@ -1,5 +1,8 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + wget https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth -O Wav2Lip/face_detection/detection/sfd/s3fd.pth mkdir -p Wav2Lip/checkpoints wget "https://iiitaphyd-my.sharepoint.com/:f:/g/personal/radrabha_m_research_iiit_ac_in/Eb3LEzbfuKlJiR600lQWRxgBIY27JZg80f7V9jtMfbNDaQ?download=1" -O Wav2Lip/checkpoints/wav2lip.pth wget "https://iiitaphyd-my.sharepoint.com/:f:/g/personal/radrabha_m_research_iiit_ac_in/EdjI7bZlgApMqsVoEUUXpLsBxqXbn5z8VTmoxp55YNDcIA?download=1" -O Wav2Lip/checkpoints/wav2lip_gan.pth -wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P gfpgan/experiments/pretrained_models \ No newline at end of file +wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P gfpgan/experiments/pretrained_models diff --git a/comps/animation/wav2lip/dependency/utils.py b/comps/animation/wav2lip/dependency/utils.py index a2fa68c482..0494533ce4 100644 --- a/comps/animation/wav2lip/dependency/utils.py +++ b/comps/animation/wav2lip/dependency/utils.py @@ -121,7 +121,11 @@ def get_args(): # we use version to select models, which is more user-friendly parser.add_argument("--img_size", type=int, default=96, help="size to reshape the detected face") parser.add_argument( - "-v", "--version", type=str, default="1.4", help="GFPGAN model version. Option: 1 | 1.2 | 1.3 | 1.4. Default: 1.4" + "-v", + "--version", + type=str, + default="1.4", + help="GFPGAN model version. Option: 1 | 1.2 | 1.3 | 1.4. Default: 1.4", ) parser.add_argument( "-s", "--upscale", type=int, default=2, help="The final upsampling scale of the image. Default: 2" @@ -325,10 +329,8 @@ def load_gfpgan(args, bg_upsampler): raise ValueError(f"Wrong model version {args.version}.") # determine model path - pythonpath = os.environ.get('PYTHONPATH').split(':')[0] - model_path = path.join( - pythonpath, "gfpgan/experiments/pretrained_models", model_name + ".pth" - ) + pythonpath = os.environ.get("PYTHONPATH").split(":")[0] + model_path = path.join(pythonpath, "gfpgan/experiments/pretrained_models", model_name + ".pth") if not path.isfile(model_path): model_path = path.join(pythonpath, "gfpgan/realesrgan/weights", model_name + ".pth") if not path.isfile(model_path): diff --git a/comps/animation/wav2lip/dependency/wav2lip_server.py b/comps/animation/wav2lip/dependency/wav2lip_server.py index 3ba9416220..8ddbe4bef3 100644 --- a/comps/animation/wav2lip/dependency/wav2lip_server.py +++ b/comps/animation/wav2lip/dependency/wav2lip_server.py @@ -171,8 +171,9 @@ async def animate(request: Request): # Specify device # Habana if args.device == "hpu": - import habana_frameworks.torch.hpu as hthpu import habana_frameworks.torch.core as htcore + import habana_frameworks.torch.hpu as hthpu + if hthpu.is_available(): device = "hpu" else: From 22cc67af57745225c511e599770455fd2f0495ac Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Fri, 18 Oct 2024 15:43:04 +0000 Subject: [PATCH 26/49] add versioning for all pip packages Signed-off-by: Chun Tao --- comps/animation/wav2lip/requirements.txt | 26 +++++++++++------------- 1 file changed, 12 insertions(+), 14 deletions(-) diff --git a/comps/animation/wav2lip/requirements.txt b/comps/animation/wav2lip/requirements.txt index a93f53083f..7bb6a639f5 100644 --- a/comps/animation/wav2lip/requirements.txt +++ b/comps/animation/wav2lip/requirements.txt @@ -1,11 +1,9 @@ - -# used by cloud native -aiohttp +aiohttp==3.10.10 basicsr==1.4.2 docarray[full] facexlib==0.3.0 -fastapi -ffmpeg-python +fastapi==0.115.2 +ffmpeg-python==0.2.0 gradio==4.38.1 gradio_client==1.1.0 grpcio==1.64.1 @@ -15,17 +13,17 @@ lmdb==1.5.1 numba==0.60.0 numpy==1.23.5 opencv-python==4.10.0.84 -opentelemetry-api -opentelemetry-exporter-otlp -opentelemetry-sdk -pandas -prometheus_fastapi_instrumentator -pyarrow -pydantic +opentelemetry-api==1.27.0 +opentelemetry-exporter-otlp==1.27.0 +opentelemetry-sdk==1.27.0 +pandas==2.2.3 +prometheus-fastapi-instrumentator==7.0.0 +pyarrow==17.0.0 +pydantic==2.9.2 pyyaml==6.0 scipy==1.14.0 -shortuuid -tavily-python +shortuuid==1.0.13 +tavily-python==0.5.0 tb-nightly==2.18.0a20240721 tqdm==4.66.4 yapf==0.40.2 From fd214e71c8d4f60aa1efe2ff2c3304ac839d1b15 Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Fri, 18 Oct 2024 20:48:04 +0000 Subject: [PATCH 27/49] e2e test script for animation Signed-off-by: Chun Tao --- comps/animation/wav2lip/Dockerfile | 4 +- comps/animation/wav2lip/README.md | 6 +- tests/animation/test_animation_wav2lip.sh | 82 +++++++++++++++++++++++ 3 files changed, 87 insertions(+), 5 deletions(-) create mode 100755 tests/animation/test_animation_wav2lip.sh diff --git a/comps/animation/wav2lip/Dockerfile b/comps/animation/wav2lip/Dockerfile index c03a0fd4a7..629d034106 100644 --- a/comps/animation/wav2lip/Dockerfile +++ b/comps/animation/wav2lip/Dockerfile @@ -15,10 +15,10 @@ ARG ARCH=cpu COPY comps /home/user/comps RUN pip install --no-cache-dir --upgrade pip && \ - pip install --no-cache-dir -r /home/user/comps/animation/requirements.txt ; + pip install --no-cache-dir -r /home/user/comps/animation/wav2lip/requirements.txt ; ENV PYTHONPATH=$PYTHONPATH:/home/user -WORKDIR /home/user/comps/animation +WORKDIR /home/user/comps/animation/wav2lip ENTRYPOINT ["python3", "animation.py"] \ No newline at end of file diff --git a/comps/animation/wav2lip/README.md b/comps/animation/wav2lip/README.md index 64ec4328b7..dc9167f05f 100644 --- a/comps/animation/wav2lip/README.md +++ b/comps/animation/wav2lip/README.md @@ -42,7 +42,7 @@ export ANIMATION_PORT=9066 export INFERENCE_MODE='wav2lip+gfpgan' export CHECKPOINT_PATH='/usr/local/lib/python3.11/site-packages/Wav2Lip/checkpoints/wav2lip_gan.pth' # export CHECKPOINT_PATH='/usr/local/lib/python3.10/dist-packages/Wav2Lip/checkpoints/wav2lip_gan.pth' -export FACE='assets/avatar1.jpg' +export FACE="$(pwd)/comps/animation/wav2lip/assets/avatar1.jpg" # export AUDIO='assets/eg3_ref.wav' # audio file path is optional, will use base64str in the post request as input if is 'None' export AUDIO='None' export FACESIZE=96 @@ -59,7 +59,7 @@ export FPS=10 - Xeon CPU ```bash -docker run --privileged -d -p 7860:7860 --ipc=host --name "wav2lip-service" -v /var/run/docker.sock:/var/run/docker.sock -v /usr/bin/docker:/usr/bin/docker -v $(pwd):$(pwd) -w /home/user/comps/animation -e PYTHON=/usr/bin/python3.11 -e DEVICE=$DEVICE -e INFERENCE_MODE=$INFERENCE_MODE -e CHECKPOINT_PATH=$CHECKPOINT_PATH -e FACE=$FACE -e AUDIO=$AUDIO -e FACESIZE=$FACESIZE -e OUTFILE=$OUTFILE -e GFPGAN_MODEL_VERSION=$GFPGAN_MODEL_VERSION -e UPSCALE_FACTOR=$UPSCALE_FACTOR -e FPS=$FPS -e WAV2LIP_PORT=$WAV2LIP_PORT opea/wav2lip:latest +docker run --privileged -d -p 7860:7860 --ipc=host --name "wav2lip-service" -w /home/user/comps/animation/wav2lip -e PYTHON=/usr/bin/python3.11 -e DEVICE=$DEVICE -e INFERENCE_MODE=$INFERENCE_MODE -e CHECKPOINT_PATH=$CHECKPOINT_PATH -e FACE=$FACE -e AUDIO=$AUDIO -e FACESIZE=$FACESIZE -e OUTFILE=$OUTFILE -e GFPGAN_MODEL_VERSION=$GFPGAN_MODEL_VERSION -e UPSCALE_FACTOR=$UPSCALE_FACTOR -e FPS=$FPS -e WAV2LIP_PORT=$WAV2LIP_PORT opea/wav2lip:latest ``` - Gaudi2 HPU @@ -90,7 +90,7 @@ python3 dependency/check_wav2lip_server.py ```bash cd GenAIComps export ip_address=$(hostname -I | awk '{print $1}') -curl http://${ip_address}:7860/v1/animation -X POST -H "Content-Type: application/json" -d @comps/animation/wav2lip/assets/audio/sample_question.json +curl http://${ip_address}:9066/v1/animation -X POST -H "Content-Type: application/json" -d @comps/animation/wav2lip/assets/audio/sample_question.json ``` or diff --git a/tests/animation/test_animation_wav2lip.sh b/tests/animation/test_animation_wav2lip.sh new file mode 100755 index 0000000000..ba5de4e278 --- /dev/null +++ b/tests/animation/test_animation_wav2lip.sh @@ -0,0 +1,82 @@ +#!/bin/bash +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +set -x + +WORKPATH=$PWD +ip_address=$(hostname -I | awk '{print $1}') + +function build_docker_images() { + cd $WORKPATH + echo $(pwd) + docker build -t opea/wav2lip:comps -f comps/animation/wav2lip/dependency/Dockerfile . + if [ $? -ne 0 ]; then + echo "opea/wav2lip built fail" + exit 1 + else + echo "opea/wav2lip built successful" + fi + docker build -t opea/animation:comps -f comps/animation/wav2lip/Dockerfile . + if [ $? -ne 0 ]; then + echo "opea/animation built fail" + exit 1 + else + echo "opea/animation built successful" + fi +} + +function start_service() { + unset http_proxy + # Set env vars + export ip_address=$(hostname -I | awk '{print $1}') + export DEVICE="cpu" + export WAV2LIP_PORT=7860 + export ANIMATION_PORT=9066 + export INFERENCE_MODE='wav2lip+gfpgan' + export CHECKPOINT_PATH='/usr/local/lib/python3.11/site-packages/Wav2Lip/checkpoints/wav2lip_gan.pth' + export FACE="assets/avatar1.jpg" + export AUDIO='None' + export FACESIZE=96 + export OUTFILE="assets/outputs/result.mp4" + export GFPGAN_MODEL_VERSION=1.4 # latest version, can roll back to v1.3 if needed + export UPSCALE_FACTOR=1 + export FPS=10 + + docker run -d --name="test-comps-animation-wav2lip" -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e DEVICE=$DEVICE -e INFERENCE_MODE=$INFERENCE_MODE -e CHECKPOINT_PATH=$CHECKPOINT_PATH -e FACE=$FACE -e AUDIO=$AUDIO -e FACESIZE=$FACESIZE -e OUTFILE=$OUTFILE -e GFPGAN_MODEL_VERSION=$GFPGAN_MODEL_VERSION -e UPSCALE_FACTOR=$UPSCALE_FACTOR -e FPS=$FPS -e WAV2LIP_PORT=$WAV2LIP_PORT -p 7860:7860 --ipc=host opea/wav2lip:comps + docker run -d --name="test-comps-animation" -e WAV2LIP_ENDPOINT=http://$ip_address:7860 -e http_proxy=$http_proxy -e https_proxy=$https_proxy -p 9066:9066 --ipc=host opea/animation:comps + sleep 1m +} + +function validate_microservice() { + result=$(http_proxy="" curl http://localhost:9066/v1/animation -X POST -H "Content-Type: application/json" -d @comps/animation/wav2lip/assets/audio/sample_question.json) + if [[ $result == *"result.mp4"* ]]; then + echo "Result correct." + else + echo "Result wrong." + docker logs test-comps-animation-wav2lip + docker logs test-comps-animation + exit 1 + fi +} + +function stop_docker() { + cid=$(docker ps -aq --filter "name=test-comps-animation*") + if [[ ! -z "$cid" ]]; then docker stop $cid && docker rm $cid && sleep 1s; fi +} + +function main() { + stop_docker + + build_docker_images + + start_service + + validate_microservice + + stop_docker + echo y | docker system prune + +} + +main \ No newline at end of file From 48a23f61027bf65f6666b1fec6c0abb32fdf57d4 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 18 Oct 2024 20:48:35 +0000 Subject: [PATCH 28/49] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- tests/animation/test_animation_wav2lip.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/animation/test_animation_wav2lip.sh b/tests/animation/test_animation_wav2lip.sh index ba5de4e278..8038bb9c64 100755 --- a/tests/animation/test_animation_wav2lip.sh +++ b/tests/animation/test_animation_wav2lip.sh @@ -79,4 +79,4 @@ function main() { } -main \ No newline at end of file +main From a75bcc6ae1691acfeff414645418c054fa343d6e Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Fri, 18 Oct 2024 21:10:02 +0000 Subject: [PATCH 29/49] update e2e test script Signed-off-by: Chun Tao --- tests/animation/test_animation_wav2lip.sh | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tests/animation/test_animation_wav2lip.sh b/tests/animation/test_animation_wav2lip.sh index ba5de4e278..46a168a233 100755 --- a/tests/animation/test_animation_wav2lip.sh +++ b/tests/animation/test_animation_wav2lip.sh @@ -75,7 +75,9 @@ function main() { validate_microservice stop_docker - echo y | docker system prune + + echo y | docker builder prune --all + echo y | docker image prune } From f637e1e7e71499c125359deba8d7672f5db60465 Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Fri, 18 Oct 2024 21:17:20 +0000 Subject: [PATCH 30/49] update e2e test script Signed-off-by: Chun Tao --- tests/animation/test_animation_wav2lip.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/animation/test_animation_wav2lip.sh b/tests/animation/test_animation_wav2lip.sh index 52a3a86568..3480ca3dbd 100755 --- a/tests/animation/test_animation_wav2lip.sh +++ b/tests/animation/test_animation_wav2lip.sh @@ -4,7 +4,7 @@ set -x -WORKPATH=$PWD +WORKPATH=$(dirname "$PWD") ip_address=$(hostname -I | awk '{print $1}') function build_docker_images() { From d9f152b8bd6040378a63aa5e2b455605e134ca8e Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Fri, 18 Oct 2024 22:07:38 +0000 Subject: [PATCH 31/49] update readme Signed-off-by: Chun Tao --- comps/animation/wav2lip/README.md | 36 ++++++++++++++++++++++--------- 1 file changed, 26 insertions(+), 10 deletions(-) diff --git a/comps/animation/wav2lip/README.md b/comps/animation/wav2lip/README.md index dc9167f05f..e7b2fa9118 100644 --- a/comps/animation/wav2lip/README.md +++ b/comps/animation/wav2lip/README.md @@ -33,20 +33,38 @@ docker build -t opea/animation:latest --build-arg https_proxy=$https_proxy --bui ## 1.2. Set environment variables +- Xeon CPU + ```bash export ip_address=$(hostname -I | awk '{print $1}') export DEVICE="cpu" -# export DEVICE="hpu" export WAV2LIP_PORT=7860 export ANIMATION_PORT=9066 export INFERENCE_MODE='wav2lip+gfpgan' export CHECKPOINT_PATH='/usr/local/lib/python3.11/site-packages/Wav2Lip/checkpoints/wav2lip_gan.pth' -# export CHECKPOINT_PATH='/usr/local/lib/python3.10/dist-packages/Wav2Lip/checkpoints/wav2lip_gan.pth' -export FACE="$(pwd)/comps/animation/wav2lip/assets/avatar1.jpg" +export FACE="comps/animation/wav2lip/assets/avatar1.jpg" +# export AUDIO='assets/eg3_ref.wav' # audio file path is optional, will use base64str in the post request as input if is 'None' +export AUDIO='None' +export FACESIZE=96 +export OUTFILE="comps/animation/wav2lip/assets/outputs/result.mp4" +export GFPGAN_MODEL_VERSION=1.4 # latest version, can roll back to v1.3 if needed +export UPSCALE_FACTOR=1 +export FPS=10 +``` + +- Gaudi2 HPU +```bash +export ip_address=$(hostname -I | awk '{print $1}') +export DEVICE="hpu" +export WAV2LIP_PORT=7860 +export ANIMATION_PORT=9066 +export INFERENCE_MODE='wav2lip+gfpgan' +export CHECKPOINT_PATH='/usr/local/lib/python3.10/dist-packages/Wav2Lip/checkpoints/wav2lip_gan.pth' +export FACE="comps/animation/wav2lip/assets/avatar1.jpg" # export AUDIO='assets/eg3_ref.wav' # audio file path is optional, will use base64str in the post request as input if is 'None' export AUDIO='None' export FACESIZE=96 -export OUTFILE="$(pwd)/comps/animation/wav2lip/assets/outputs/result.mp4" +export OUTFILE="comps/animation/wav2lip/assets/outputs/result.mp4" export GFPGAN_MODEL_VERSION=1.4 # latest version, can roll back to v1.3 if needed export UPSCALE_FACTOR=1 export FPS=10 @@ -59,13 +77,13 @@ export FPS=10 - Xeon CPU ```bash -docker run --privileged -d -p 7860:7860 --ipc=host --name "wav2lip-service" -w /home/user/comps/animation/wav2lip -e PYTHON=/usr/bin/python3.11 -e DEVICE=$DEVICE -e INFERENCE_MODE=$INFERENCE_MODE -e CHECKPOINT_PATH=$CHECKPOINT_PATH -e FACE=$FACE -e AUDIO=$AUDIO -e FACESIZE=$FACESIZE -e OUTFILE=$OUTFILE -e GFPGAN_MODEL_VERSION=$GFPGAN_MODEL_VERSION -e UPSCALE_FACTOR=$UPSCALE_FACTOR -e FPS=$FPS -e WAV2LIP_PORT=$WAV2LIP_PORT opea/wav2lip:latest +docker run --privileged -d --name "wav2lip-service" -p 7860:7860 --ipc=host -w /home/user/comps/animation/wav2lip -e PYTHON=/usr/bin/python3.11 -e DEVICE=$DEVICE -e INFERENCE_MODE=$INFERENCE_MODE -e CHECKPOINT_PATH=$CHECKPOINT_PATH -e FACE=$FACE -e AUDIO=$AUDIO -e FACESIZE=$FACESIZE -e OUTFILE=$OUTFILE -e GFPGAN_MODEL_VERSION=$GFPGAN_MODEL_VERSION -e UPSCALE_FACTOR=$UPSCALE_FACTOR -e FPS=$FPS -e WAV2LIP_PORT=$WAV2LIP_PORT opea/wav2lip:latest ``` - Gaudi2 HPU ```bash -docker run --privileged -d -p 7860:7860 --runtime=habana --cap-add=sys_nice --net=host --ipc=host --name "wav2lip-gaudi-service" -v /var/run/docker.sock:/var/run/docker.sock -v /usr/bin/docker:/usr/bin/docker -v $(pwd):$(pwd) -w /home/user/comps/animation -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e PYTHON=/usr/bin/python3.10 -e DEVICE=$DEVICE -e INFERENCE_MODE=$INFERENCE_MODE -e CHECKPOINT_PATH=$CHECKPOINT_PATH -e FACE=$FACE -e AUDIO=$AUDIO -e FACESIZE=$FACESIZE -e OUTFILE=$OUTFILE -e GFPGAN_MODEL_VERSION=$GFPGAN_MODEL_VERSION -e UPSCALE_FACTOR=$UPSCALE_FACTOR -e FPS=$FPS -e WAV2LIP_PORT=$WAV2LIP_PORT opea/wav2lip-gaudi:latest +docker run --privileged -d --name "wav2lip-gaudi-service" -p 7860:7860 --runtime=habana --cap-add=sys_nice --net=host --ipc=host -w /home/user/comps/animation/wav2lip -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e PYTHON=/usr/bin/python3.10 -e DEVICE=$DEVICE -e INFERENCE_MODE=$INFERENCE_MODE -e CHECKPOINT_PATH=$CHECKPOINT_PATH -e FACE=$FACE -e AUDIO=$AUDIO -e FACESIZE=$FACESIZE -e OUTFILE=$OUTFILE -e GFPGAN_MODEL_VERSION=$GFPGAN_MODEL_VERSION -e UPSCALE_FACTOR=$UPSCALE_FACTOR -e FPS=$FPS -e WAV2LIP_PORT=$WAV2LIP_PORT opea/wav2lip-gaudi:latest ``` ## 2.2 Run Animation Microservice @@ -103,9 +121,7 @@ python3 check_animation_server.py The expected output is a message similar to the following: ```bash -"Status code: 200" -"Check $OUTFILE for the result." -"{'id': '33dd8249228b0e011a33b449af9aa776', 'video_save_path': '.../GenAIComps/comps/animation/wav2lip/assets/outputs/result.mp4'}" +{'wav2lip_result': '.../GenAIComps/comps/animation/wav2lip/assets/outputs/result.mp4'} ``` -Please find "./outputs/result.mp4" as a reference generated video. +Please find "comps/animation/wav2lip/assets/outputs/result.mp4" as a reference generated video. From f2e6b346b74a8b48e8e7f471613ddadaa8372102 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 18 Oct 2024 22:08:01 +0000 Subject: [PATCH 32/49] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- comps/animation/wav2lip/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/comps/animation/wav2lip/README.md b/comps/animation/wav2lip/README.md index e7b2fa9118..ac10107d28 100644 --- a/comps/animation/wav2lip/README.md +++ b/comps/animation/wav2lip/README.md @@ -53,6 +53,7 @@ export FPS=10 ``` - Gaudi2 HPU + ```bash export ip_address=$(hostname -I | awk '{print $1}') export DEVICE="hpu" From 72a65275e27c508eaf92d76f5d700a25f79a5069 Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Sat, 19 Oct 2024 00:58:09 +0000 Subject: [PATCH 33/49] update Signed-off-by: Chun Tao --- comps/animation/wav2lip/README.md | 8 ++++---- .../wav2lip/assets/{ => audio}/eg3_ref.wav | Bin .../wav2lip/assets/{ => img}/avatar1.jpg | Bin .../wav2lip/assets/{ => img}/avatar2.jpg | Bin .../wav2lip/assets/{ => img}/avatar3.png | Bin .../wav2lip/assets/{ => img}/avatar4.png | Bin .../wav2lip/assets/{ => img}/avatar5.png | Bin .../wav2lip/assets/{ => img}/avatar6.png | Bin .../wav2lip/assets/{ => img}/flowchart.png | Bin .../wav2lip/assets/{ => img}/gaudi.png | Bin .../wav2lip/assets/{ => img}/opea_gh_qr.png | Bin .../wav2lip/assets/{ => img}/opea_qr.png | Bin .../animation/wav2lip/assets/{ => img}/xeon.jpg | Bin .../wav2lip/dependency/Dockerfile.intel_hpu | 16 +++++----------- tests/animation/test_animation_wav2lip.sh | 2 +- 15 files changed, 10 insertions(+), 16 deletions(-) rename comps/animation/wav2lip/assets/{ => audio}/eg3_ref.wav (100%) rename comps/animation/wav2lip/assets/{ => img}/avatar1.jpg (100%) rename comps/animation/wav2lip/assets/{ => img}/avatar2.jpg (100%) rename comps/animation/wav2lip/assets/{ => img}/avatar3.png (100%) rename comps/animation/wav2lip/assets/{ => img}/avatar4.png (100%) rename comps/animation/wav2lip/assets/{ => img}/avatar5.png (100%) rename comps/animation/wav2lip/assets/{ => img}/avatar6.png (100%) rename comps/animation/wav2lip/assets/{ => img}/flowchart.png (100%) rename comps/animation/wav2lip/assets/{ => img}/gaudi.png (100%) rename comps/animation/wav2lip/assets/{ => img}/opea_gh_qr.png (100%) rename comps/animation/wav2lip/assets/{ => img}/opea_qr.png (100%) rename comps/animation/wav2lip/assets/{ => img}/xeon.jpg (100%) diff --git a/comps/animation/wav2lip/README.md b/comps/animation/wav2lip/README.md index ac10107d28..70fb887069 100644 --- a/comps/animation/wav2lip/README.md +++ b/comps/animation/wav2lip/README.md @@ -42,8 +42,8 @@ export WAV2LIP_PORT=7860 export ANIMATION_PORT=9066 export INFERENCE_MODE='wav2lip+gfpgan' export CHECKPOINT_PATH='/usr/local/lib/python3.11/site-packages/Wav2Lip/checkpoints/wav2lip_gan.pth' -export FACE="comps/animation/wav2lip/assets/avatar1.jpg" -# export AUDIO='assets/eg3_ref.wav' # audio file path is optional, will use base64str in the post request as input if is 'None' +export FACE="comps/animation/wav2lip/assets/img/avatar1.jpg" +# export AUDIO='assets/audio/eg3_ref.wav' # audio file path is optional, will use base64str in the post request as input if is 'None' export AUDIO='None' export FACESIZE=96 export OUTFILE="comps/animation/wav2lip/assets/outputs/result.mp4" @@ -61,8 +61,8 @@ export WAV2LIP_PORT=7860 export ANIMATION_PORT=9066 export INFERENCE_MODE='wav2lip+gfpgan' export CHECKPOINT_PATH='/usr/local/lib/python3.10/dist-packages/Wav2Lip/checkpoints/wav2lip_gan.pth' -export FACE="comps/animation/wav2lip/assets/avatar1.jpg" -# export AUDIO='assets/eg3_ref.wav' # audio file path is optional, will use base64str in the post request as input if is 'None' +export FACE="comps/animation/wav2lip/assets/img/avatar1.jpg" +# export AUDIO='assets/audio/eg3_ref.wav' # audio file path is optional, will use base64str in the post request as input if is 'None' export AUDIO='None' export FACESIZE=96 export OUTFILE="comps/animation/wav2lip/assets/outputs/result.mp4" diff --git a/comps/animation/wav2lip/assets/eg3_ref.wav b/comps/animation/wav2lip/assets/audio/eg3_ref.wav similarity index 100% rename from comps/animation/wav2lip/assets/eg3_ref.wav rename to comps/animation/wav2lip/assets/audio/eg3_ref.wav diff --git a/comps/animation/wav2lip/assets/avatar1.jpg b/comps/animation/wav2lip/assets/img/avatar1.jpg similarity index 100% rename from comps/animation/wav2lip/assets/avatar1.jpg rename to comps/animation/wav2lip/assets/img/avatar1.jpg diff --git a/comps/animation/wav2lip/assets/avatar2.jpg b/comps/animation/wav2lip/assets/img/avatar2.jpg similarity index 100% rename from comps/animation/wav2lip/assets/avatar2.jpg rename to comps/animation/wav2lip/assets/img/avatar2.jpg diff --git a/comps/animation/wav2lip/assets/avatar3.png b/comps/animation/wav2lip/assets/img/avatar3.png similarity index 100% rename from comps/animation/wav2lip/assets/avatar3.png rename to comps/animation/wav2lip/assets/img/avatar3.png diff --git a/comps/animation/wav2lip/assets/avatar4.png b/comps/animation/wav2lip/assets/img/avatar4.png similarity index 100% rename from comps/animation/wav2lip/assets/avatar4.png rename to comps/animation/wav2lip/assets/img/avatar4.png diff --git a/comps/animation/wav2lip/assets/avatar5.png b/comps/animation/wav2lip/assets/img/avatar5.png similarity index 100% rename from comps/animation/wav2lip/assets/avatar5.png rename to comps/animation/wav2lip/assets/img/avatar5.png diff --git a/comps/animation/wav2lip/assets/avatar6.png b/comps/animation/wav2lip/assets/img/avatar6.png similarity index 100% rename from comps/animation/wav2lip/assets/avatar6.png rename to comps/animation/wav2lip/assets/img/avatar6.png diff --git a/comps/animation/wav2lip/assets/flowchart.png b/comps/animation/wav2lip/assets/img/flowchart.png similarity index 100% rename from comps/animation/wav2lip/assets/flowchart.png rename to comps/animation/wav2lip/assets/img/flowchart.png diff --git a/comps/animation/wav2lip/assets/gaudi.png b/comps/animation/wav2lip/assets/img/gaudi.png similarity index 100% rename from comps/animation/wav2lip/assets/gaudi.png rename to comps/animation/wav2lip/assets/img/gaudi.png diff --git a/comps/animation/wav2lip/assets/opea_gh_qr.png b/comps/animation/wav2lip/assets/img/opea_gh_qr.png similarity index 100% rename from comps/animation/wav2lip/assets/opea_gh_qr.png rename to comps/animation/wav2lip/assets/img/opea_gh_qr.png diff --git a/comps/animation/wav2lip/assets/opea_qr.png b/comps/animation/wav2lip/assets/img/opea_qr.png similarity index 100% rename from comps/animation/wav2lip/assets/opea_qr.png rename to comps/animation/wav2lip/assets/img/opea_qr.png diff --git a/comps/animation/wav2lip/assets/xeon.jpg b/comps/animation/wav2lip/assets/img/xeon.jpg similarity index 100% rename from comps/animation/wav2lip/assets/xeon.jpg rename to comps/animation/wav2lip/assets/img/xeon.jpg diff --git a/comps/animation/wav2lip/dependency/Dockerfile.intel_hpu b/comps/animation/wav2lip/dependency/Dockerfile.intel_hpu index 5439e9d9e1..86fb7d9490 100644 --- a/comps/animation/wav2lip/dependency/Dockerfile.intel_hpu +++ b/comps/animation/wav2lip/dependency/Dockerfile.intel_hpu @@ -19,7 +19,7 @@ RUN rm -rf /var/lib/apt/lists/* # Install GenAIComps RUN mkdir -p /home/user/comps COPY comps /home/user/comps -COPY comps/animation/dependency/entrypoint.sh /usr/local/bin/entrypoint.sh +COPY comps/animation/wav2lip/dependency/entrypoint.sh /usr/local/bin/entrypoint.sh # Install ffmpeg with x264 software codec RUN git clone https://github.com/FFmpeg/FFmpeg.git /home/user/comps/animation/FFmpeg @@ -44,18 +44,12 @@ RUN touch /usr/local/lib/python3.10/dist-packages/gfpgan/__init__.py ENV PYTHONPATH="$PYTHONPATH:/usr/local/lib/python3.10/dist-packages/gfpgan" # Download pre-trained models -# Install gdown -RUN pip install gdown WORKDIR /usr/local/lib/python3.10/dist-packages -# RUN wget https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth -O Wav2Lip/face_detection/detection/sfd/s3fd.pth -RUN gdown https://drive.google.com/uc?id=1wwts8U4Wx2lpLOI9uGcUB-TLmKRIFnP8 -O Wav2Lip/face_detection/detection/sfd/s3fd.pth -RUN mkdir -p Wav2Lip/checkpoints -RUN gdown https://drive.google.com/uc?id=1mIKfu_onFKbkbrq6cKVRBblXe5bNMhd9 -O Wav2Lip/checkpoints/wav2lip.pth -RUN gdown https://drive.google.com/uc?id=1DD7dtUfNWqNoW-2Gnp78B4PyaiJ5ybhP -O Wav2Lip/checkpoints/wav2lip_gan.pth -RUN wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P gfpgan/experiments/pretrained_models +RUN chmod +x /home/user/comps/animation/wav2lip/dependency/download_ckpts.sh +RUN /home/user/comps/animation/wav2lip/dependency/download_ckpts.sh # Install pip dependencies -RUN pip install -r /home/user/comps/animation/requirements.txt +RUN pip install -r /home/user/comps/animation/wav2lip/requirements.txt # Custom patches # Modify the degradations.py file to import rgb_to_grayscale from torchvision.transforms.functional @@ -68,7 +62,7 @@ RUN sed -i "s/if 'cpu' not in device and 'cuda' not in device:/if 'cpu' not in d RUN sed -i 's/hp.sample_rate, hp.n_fft/sr=hp.sample_rate, n_fft=hp.n_fft/' /usr/local/lib/python3.10/dist-packages/Wav2Lip/audio.py # Set the working directory -WORKDIR /home/user/comps/animation +WORKDIR /home/user/comps/animation/wav2lip # Define the command to run when the container starts RUN chmod +x /usr/local/bin/entrypoint.sh diff --git a/tests/animation/test_animation_wav2lip.sh b/tests/animation/test_animation_wav2lip.sh index 3480ca3dbd..3daf367b60 100755 --- a/tests/animation/test_animation_wav2lip.sh +++ b/tests/animation/test_animation_wav2lip.sh @@ -35,7 +35,7 @@ function start_service() { export ANIMATION_PORT=9066 export INFERENCE_MODE='wav2lip+gfpgan' export CHECKPOINT_PATH='/usr/local/lib/python3.11/site-packages/Wav2Lip/checkpoints/wav2lip_gan.pth' - export FACE="assets/avatar1.jpg" + export FACE="assets/img/avatar1.jpg" export AUDIO='None' export FACESIZE=96 export OUTFILE="assets/outputs/result.mp4" From dd849ef6cfa85137d6b3e346ab49ac213836c3eb Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Sat, 19 Oct 2024 04:29:00 +0000 Subject: [PATCH 34/49] update Signed-off-by: Chun Tao --- comps/__init__.py | 1 + comps/cores/mega/constants.py | 1 + comps/cores/mega/gateway.py | 33 +++++++++++++++++++++++++++++++++ 3 files changed, 35 insertions(+) diff --git a/comps/__init__.py b/comps/__init__.py index 46f5f78e09..a6ef41f65b 100644 --- a/comps/__init__.py +++ b/comps/__init__.py @@ -58,6 +58,7 @@ VideoQnAGateway, VisualQnAGateway, MultimodalQnAGateway, + AvatarChatbotGateway, ) # Telemetry diff --git a/comps/cores/mega/constants.py b/comps/cores/mega/constants.py index fded5e8114..e93c6d55cc 100644 --- a/comps/cores/mega/constants.py +++ b/comps/cores/mega/constants.py @@ -60,6 +60,7 @@ class MegaServiceEndpoint(Enum): GUARDRAILS = "/v1/guardrails" RAGAS = "/v1/ragas" GRAPHS = "/v1/graphs" + AVATAR_CHATBOT = "v1/avatarchatbot" # COMMON LIST_SERVICE = "/v1/list_service" diff --git a/comps/cores/mega/gateway.py b/comps/cores/mega/gateway.py index 92d47aa583..a1357764df 100644 --- a/comps/cores/mega/gateway.py +++ b/comps/cores/mega/gateway.py @@ -858,3 +858,36 @@ async def handle_request(self, request: Request): ) ) return ChatCompletionResponse(model="multimodalqna", choices=choices, usage=usage) + + +class AvatarChatbotGateway(Gateway): + def __init__(self, megaservice, host="0.0.0.0", port=8888): + super().__init__( + megaservice, + host, + port, + str(MegaServiceEndpoint.AVATAR_CHATBOT), + AudioChatCompletionRequest, + ChatCompletionResponse, + ) + + async def handle_request(self, request: Request): + data = await request.json() + + chat_request = AudioChatCompletionRequest.model_validate(data) + parameters = LLMParams( + # relatively lower max_tokens for audio conversation + max_new_tokens=chat_request.max_tokens if chat_request.max_tokens else 128, + top_k=chat_request.top_k if chat_request.top_k else 10, + top_p=chat_request.top_p if chat_request.top_p else 0.95, + temperature=chat_request.temperature if chat_request.temperature else 0.01, + repetition_penalty=chat_request.presence_penalty if chat_request.presence_penalty else 1.03, + streaming=False, # TODO add streaming LLM output as input to TTS + ) + result_dict, runtime_graph = await self.megaservice.schedule( + initial_inputs={"byte_str": chat_request.byte_str}, llm_parameters=parameters + ) + + last_node = runtime_graph.all_leaves()[-1] + response = result_dict[last_node]["wav2lip_result"] + return response \ No newline at end of file From fecedab09be76bc10bb61fb710541985dfae4e2e Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sat, 19 Oct 2024 04:29:59 +0000 Subject: [PATCH 35/49] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- comps/cores/mega/gateway.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comps/cores/mega/gateway.py b/comps/cores/mega/gateway.py index a1357764df..aa0cdf76de 100644 --- a/comps/cores/mega/gateway.py +++ b/comps/cores/mega/gateway.py @@ -890,4 +890,4 @@ async def handle_request(self, request: Request): last_node = runtime_graph.all_leaves()[-1] response = result_dict[last_node]["wav2lip_result"] - return response \ No newline at end of file + return response From f3f334319ee034de6c6a86b6778537bd0842dbe4 Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Sat, 19 Oct 2024 04:51:20 +0000 Subject: [PATCH 36/49] update gateway Signed-off-by: Chun Tao --- comps/cores/mega/gateway.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comps/cores/mega/gateway.py b/comps/cores/mega/gateway.py index aa0cdf76de..95ef2637aa 100644 --- a/comps/cores/mega/gateway.py +++ b/comps/cores/mega/gateway.py @@ -889,5 +889,5 @@ async def handle_request(self, request: Request): ) last_node = runtime_graph.all_leaves()[-1] - response = result_dict[last_node]["wav2lip_result"] + response = result_dict[last_node]["video_path"] return response From 8790139b979a1973d3709b4fdcdf710c1f234aa5 Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Sat, 19 Oct 2024 05:19:14 +0000 Subject: [PATCH 37/49] udpate gateway Signed-off-by: Chun Tao --- comps/cores/mega/gateway.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comps/cores/mega/gateway.py b/comps/cores/mega/gateway.py index 95ef2637aa..27c96771e7 100644 --- a/comps/cores/mega/gateway.py +++ b/comps/cores/mega/gateway.py @@ -874,7 +874,7 @@ def __init__(self, megaservice, host="0.0.0.0", port=8888): async def handle_request(self, request: Request): data = await request.json() - chat_request = AudioChatCompletionRequest.model_validate(data) + chat_request = AudioChatCompletionRequest.parse_obj(data) parameters = LLMParams( # relatively lower max_tokens for audio conversation max_new_tokens=chat_request.max_tokens if chat_request.max_tokens else 128, From 26deb8809d61c655128e33fe8e9f17cccc3b08e3 Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Sat, 19 Oct 2024 05:34:49 +0000 Subject: [PATCH 38/49] Fix AVATAR_CHATBOT Signed-off-by: Chun Tao --- comps/cores/mega/constants.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comps/cores/mega/constants.py b/comps/cores/mega/constants.py index e93c6d55cc..0917bd46c5 100644 --- a/comps/cores/mega/constants.py +++ b/comps/cores/mega/constants.py @@ -60,7 +60,7 @@ class MegaServiceEndpoint(Enum): GUARDRAILS = "/v1/guardrails" RAGAS = "/v1/ragas" GRAPHS = "/v1/graphs" - AVATAR_CHATBOT = "v1/avatarchatbot" + AVATAR_CHATBOT = "/v1/avatarchatbot" # COMMON LIST_SERVICE = "/v1/list_service" From 5d8fef882aad6f7d17a14febd1df33dd0d488890 Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Sat, 19 Oct 2024 05:43:37 +0000 Subject: [PATCH 39/49] update gateway Signed-off-by: Chun Tao --- comps/cores/mega/gateway.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comps/cores/mega/gateway.py b/comps/cores/mega/gateway.py index 27c96771e7..babeef3f79 100644 --- a/comps/cores/mega/gateway.py +++ b/comps/cores/mega/gateway.py @@ -874,7 +874,7 @@ def __init__(self, megaservice, host="0.0.0.0", port=8888): async def handle_request(self, request: Request): data = await request.json() - chat_request = AudioChatCompletionRequest.parse_obj(data) + chat_request = AudioChatCompletionRequest.model_validate(data) parameters = LLMParams( # relatively lower max_tokens for audio conversation max_new_tokens=chat_request.max_tokens if chat_request.max_tokens else 128, @@ -885,7 +885,7 @@ async def handle_request(self, request: Request): streaming=False, # TODO add streaming LLM output as input to TTS ) result_dict, runtime_graph = await self.megaservice.schedule( - initial_inputs={"byte_str": chat_request.byte_str}, llm_parameters=parameters + initial_inputs={"byte_str": chat_request.audio}, llm_parameters=parameters ) last_node = runtime_graph.all_leaves()[-1] From a0b4d13f81c5da22b2b0992979d484c1277acddc Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Sat, 19 Oct 2024 16:20:37 +0000 Subject: [PATCH 40/49] update Signed-off-by: Chun Tao --- comps/cores/mega/gateway.py | 1 + 1 file changed, 1 insertion(+) diff --git a/comps/cores/mega/gateway.py b/comps/cores/mega/gateway.py index babeef3f79..b63570ee0b 100644 --- a/comps/cores/mega/gateway.py +++ b/comps/cores/mega/gateway.py @@ -884,6 +884,7 @@ async def handle_request(self, request: Request): repetition_penalty=chat_request.presence_penalty if chat_request.presence_penalty else 1.03, streaming=False, # TODO add streaming LLM output as input to TTS ) + print(parameters) result_dict, runtime_graph = await self.megaservice.schedule( initial_inputs={"byte_str": chat_request.audio}, llm_parameters=parameters ) From 9a705ca3b10675eb06a012f34c022f390cf796a2 Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Sat, 19 Oct 2024 16:24:27 +0000 Subject: [PATCH 41/49] test Signed-off-by: Chun Tao --- comps/cores/mega/gateway.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comps/cores/mega/gateway.py b/comps/cores/mega/gateway.py index b63570ee0b..583bab47b5 100644 --- a/comps/cores/mega/gateway.py +++ b/comps/cores/mega/gateway.py @@ -877,7 +877,7 @@ async def handle_request(self, request: Request): chat_request = AudioChatCompletionRequest.model_validate(data) parameters = LLMParams( # relatively lower max_tokens for audio conversation - max_new_tokens=chat_request.max_tokens if chat_request.max_tokens else 128, + max_new_tokens=128, top_k=chat_request.top_k if chat_request.top_k else 10, top_p=chat_request.top_p if chat_request.top_p else 0.95, temperature=chat_request.temperature if chat_request.temperature else 0.01, From e177ba2c0725a86a8600b54f0a81ded170fa1c87 Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Sat, 19 Oct 2024 16:26:36 +0000 Subject: [PATCH 42/49] update Signed-off-by: Chun Tao --- comps/cores/mega/gateway.py | 1 + 1 file changed, 1 insertion(+) diff --git a/comps/cores/mega/gateway.py b/comps/cores/mega/gateway.py index 583bab47b5..c35ae40ee0 100644 --- a/comps/cores/mega/gateway.py +++ b/comps/cores/mega/gateway.py @@ -877,6 +877,7 @@ async def handle_request(self, request: Request): chat_request = AudioChatCompletionRequest.model_validate(data) parameters = LLMParams( # relatively lower max_tokens for audio conversation + # max_new_tokens=chat_request.max_tokens if chat_request.max_tokens else 128, max_new_tokens=128, top_k=chat_request.top_k if chat_request.top_k else 10, top_p=chat_request.top_p if chat_request.top_p else 0.95, From d36d0fec49165944b91b876e70d043e0c16c8eeb Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Sat, 19 Oct 2024 16:26:54 +0000 Subject: [PATCH 43/49] update Signed-off-by: Chun Tao --- comps/cores/mega/gateway.py | 1 - 1 file changed, 1 deletion(-) diff --git a/comps/cores/mega/gateway.py b/comps/cores/mega/gateway.py index c35ae40ee0..a1db37e03f 100644 --- a/comps/cores/mega/gateway.py +++ b/comps/cores/mega/gateway.py @@ -885,7 +885,6 @@ async def handle_request(self, request: Request): repetition_penalty=chat_request.presence_penalty if chat_request.presence_penalty else 1.03, streaming=False, # TODO add streaming LLM output as input to TTS ) - print(parameters) result_dict, runtime_graph = await self.megaservice.schedule( initial_inputs={"byte_str": chat_request.audio}, llm_parameters=parameters ) From e90f5cf650bd2877b12cd1a46637fc9d3c02be3d Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Sat, 19 Oct 2024 16:31:11 +0000 Subject: [PATCH 44/49] update gateway Signed-off-by: Chun Tao --- comps/cores/mega/gateway.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comps/cores/mega/gateway.py b/comps/cores/mega/gateway.py index a1db37e03f..e4cac0338f 100644 --- a/comps/cores/mega/gateway.py +++ b/comps/cores/mega/gateway.py @@ -877,8 +877,8 @@ async def handle_request(self, request: Request): chat_request = AudioChatCompletionRequest.model_validate(data) parameters = LLMParams( # relatively lower max_tokens for audio conversation - # max_new_tokens=chat_request.max_tokens if chat_request.max_tokens else 128, - max_new_tokens=128, + max_new_tokens=chat_request.max_tokens if chat_request.max_tokens else 128, + # max_new_tokens=128, top_k=chat_request.top_k if chat_request.top_k else 10, top_p=chat_request.top_p if chat_request.top_p else 0.95, temperature=chat_request.temperature if chat_request.temperature else 0.01, From dda2e31711b2396af2e52429cbecfa25b3d0a527 Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Sat, 19 Oct 2024 16:42:18 +0000 Subject: [PATCH 45/49] fix max_tokens in AvatarChatbot gateway Signed-off-by: Chun Tao --- comps/cores/mega/gateway.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comps/cores/mega/gateway.py b/comps/cores/mega/gateway.py index e4cac0338f..a1db37e03f 100644 --- a/comps/cores/mega/gateway.py +++ b/comps/cores/mega/gateway.py @@ -877,8 +877,8 @@ async def handle_request(self, request: Request): chat_request = AudioChatCompletionRequest.model_validate(data) parameters = LLMParams( # relatively lower max_tokens for audio conversation - max_new_tokens=chat_request.max_tokens if chat_request.max_tokens else 128, - # max_new_tokens=128, + # max_new_tokens=chat_request.max_tokens if chat_request.max_tokens else 128, + max_new_tokens=128, top_k=chat_request.top_k if chat_request.top_k else 10, top_p=chat_request.top_p if chat_request.top_p else 0.95, temperature=chat_request.temperature if chat_request.temperature else 0.01, From 83186dea8739a52b1eaa3d27d9ebbf62fed71b94 Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Sat, 19 Oct 2024 16:46:59 +0000 Subject: [PATCH 46/49] test Signed-off-by: Chun Tao --- comps/cores/mega/gateway.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/comps/cores/mega/gateway.py b/comps/cores/mega/gateway.py index a1db37e03f..a35b0928a8 100644 --- a/comps/cores/mega/gateway.py +++ b/comps/cores/mega/gateway.py @@ -875,6 +875,7 @@ async def handle_request(self, request: Request): data = await request.json() chat_request = AudioChatCompletionRequest.model_validate(data) + print(chat_request) parameters = LLMParams( # relatively lower max_tokens for audio conversation # max_new_tokens=chat_request.max_tokens if chat_request.max_tokens else 128, @@ -885,6 +886,7 @@ async def handle_request(self, request: Request): repetition_penalty=chat_request.presence_penalty if chat_request.presence_penalty else 1.03, streaming=False, # TODO add streaming LLM output as input to TTS ) + print(parameters) result_dict, runtime_graph = await self.megaservice.schedule( initial_inputs={"byte_str": chat_request.audio}, llm_parameters=parameters ) From 38e4d462277a57f603dafeba19ba529320627983 Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Sat, 19 Oct 2024 16:51:16 +0000 Subject: [PATCH 47/49] update Signed-off-by: Chun Tao --- comps/cores/mega/gateway.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/comps/cores/mega/gateway.py b/comps/cores/mega/gateway.py index a35b0928a8..69598dec34 100644 --- a/comps/cores/mega/gateway.py +++ b/comps/cores/mega/gateway.py @@ -875,11 +875,9 @@ async def handle_request(self, request: Request): data = await request.json() chat_request = AudioChatCompletionRequest.model_validate(data) - print(chat_request) parameters = LLMParams( # relatively lower max_tokens for audio conversation - # max_new_tokens=chat_request.max_tokens if chat_request.max_tokens else 128, - max_new_tokens=128, + max_tokens=chat_request.max_tokens if chat_request.max_tokens else 128, top_k=chat_request.top_k if chat_request.top_k else 10, top_p=chat_request.top_p if chat_request.top_p else 0.95, temperature=chat_request.temperature if chat_request.temperature else 0.01, From 7aa56f4f0bd35a390596dbfcc90c507a716e1c6b Mon Sep 17 00:00:00 2001 From: Chun Tao Date: Sat, 19 Oct 2024 16:55:31 +0000 Subject: [PATCH 48/49] update Signed-off-by: Chun Tao --- comps/cores/mega/gateway.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/comps/cores/mega/gateway.py b/comps/cores/mega/gateway.py index 69598dec34..146271cbcf 100644 --- a/comps/cores/mega/gateway.py +++ b/comps/cores/mega/gateway.py @@ -884,7 +884,8 @@ async def handle_request(self, request: Request): repetition_penalty=chat_request.presence_penalty if chat_request.presence_penalty else 1.03, streaming=False, # TODO add streaming LLM output as input to TTS ) - print(parameters) + # print(parameters) + result_dict, runtime_graph = await self.megaservice.schedule( initial_inputs={"byte_str": chat_request.audio}, llm_parameters=parameters ) From 155ef8eac63adc56dbe6506e019cd6d6fb952a29 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sat, 19 Oct 2024 16:56:05 +0000 Subject: [PATCH 49/49] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- comps/cores/mega/gateway.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comps/cores/mega/gateway.py b/comps/cores/mega/gateway.py index 146271cbcf..d0ec45324b 100644 --- a/comps/cores/mega/gateway.py +++ b/comps/cores/mega/gateway.py @@ -885,7 +885,7 @@ async def handle_request(self, request: Request): streaming=False, # TODO add streaming LLM output as input to TTS ) # print(parameters) - + result_dict, runtime_graph = await self.megaservice.schedule( initial_inputs={"byte_str": chat_request.audio}, llm_parameters=parameters )

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